library(tidyverse)
library(data.table)
library(ggh4x)
library(lme4)
library(car)
library(ggeffects)
library(doParallel)

cores <- getOption("mc.cores", detectCores()) # for parallel computation
cl <- makeCluster(cores)
registerDoParallel(cl)

data loading

dat <- fread("exp1_data_eyetracking.csv", header = TRUE)
dat <- subset(dat, dat$event == "fixation" & dat$conf != 0) # conf should be NA
dat$condition <- as.factor(dat$condition)
dat$fixItem <- factor(dat$fixItem, levels = c("target", "distractor", "dud", "other"))
dat$chosenItem <- factor(dat$chosenItem, levels = c("target", "distractor", "dud"))
dat %>% group_by(subj) %>% mutate(conf_normalized = scale(conf)) -> dat # subject-wise normalization of confidence 
dat %>% mutate(q_dur_distractor = ifelse(dur_distractor > quantile(dur_distractor)[4], 0.75,
                                  ifelse(dur_distractor <= quantile(dur_distractor)[4] & dur_distractor > quantile(dur_distractor)[3], 0.5,
                                  ifelse(dur_distractor <= quantile(dur_distractor)[3] & dur_distractor > quantile(dur_distractor)[2], 0.25, 0)))) -> dat
dat %>% mutate(tdDurationRatio = dur_target/dur_distractor) -> dat

subject-wise fixation plot

plot1 <- foreach(i = unique(dat$subj), .packages = c("tidyverse", "ggh4x")) %dopar% {
    subset(dat, dat$subj == i) %>%
        ggplot() + geom_point(aes(x = x, y = y, size = dur, color = condition), alpha = 0.3) + 
        facet_nested(. ~ targetPos + dudPos) + ggtitle(i) %>% print()
}
plot1
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Position-based fixation frequency

dat %>%
    group_by(condition, targetPos, dudPos, fixItem, subj) %>%
    summarise(n = n()) %>%
    ungroup() -> freq # ungroup() is necessary for plotting dud fix data
## `summarise()` has grouped output by 'condition', 'targetPos', 'dudPos',
## 'fixItem'. You can override using the `.groups` argument.
plot2 <- foreach(i = unique(freq$condition), .packages = c("tidyverse", "ggh4x")) %dopar% {
    p <- ggplot(subset(freq, freq$condition == i)) + 
        geom_violin(aes(x = fixItem, y = n, color = fixItem)) + 
        geom_point(aes(x = fixItem, y = n, color = fixItem)) + 
        theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        facet_nested(. ~ targetPos + dudPos) + ggtitle(i) %>% print()
}
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All trials

dat %>%
    group_by(subj, condition) %>%
    mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
    group_by(fixItem, condition, subj) %>%
    mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
    select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
    distinct() -> fd1
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency
ggplot(fd1) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt, color = subj)) + 
    xlab("") + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)

# condition-wise fixation frequency
ggplot(fd1) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) + 
    theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)

AOI only

# total fixation frequency (AOI only) other以外へのfixationがなかった試行は除かれる
dat %>%
    filter(., fixItem != "other") %>%
    group_by(subj, condition) %>%
    mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
    group_by(fixItem, condition, subj) %>%
    mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
    select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
    distinct() -> fd2
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency (AOI only)
ggplot(fd2) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt, color = subj)) + 
    xlab("") + ylab("Mean fixations per trial (AOI only)") + facet_wrap(. ~ condition)

# condition-wise fixation frequency (AOI only)   
ggplot(fd2) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) + 
    theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixation per trial (AOI only)") + facet_wrap(. ~ condition)

Target and distractor only

dat %>%
    filter(., fixItem != "other" & fixItem != "dud") %>%
    group_by(subj, condition) %>%
    mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
    group_by(fixItem, condition, subj) %>%
    mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
    select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
    distinct() -> fd3
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency 
ggplot(fd3) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt)) + 
    xlab("") + ylab("Mean fixations per trial (target and distractor only)") + facet_wrap(. ~ condition)

# condition-wise fixation frequency (target and distractor only)   
ggplot(fd3) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) + 
    theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixations per trial (target and distractor only)") + facet_wrap(. ~ condition)

Stimulus-based fixation frequency (choice considered)

dat %>%
    group_by(subj, condition, chosenItem) %>%
    mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
    group_by(fixItem, condition, subj, chosenItem) %>%
    mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
    select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
    distinct() -> fd4
## Adding missing grouping variables: `fixItem`, `condition`, `subj`, `chosenItem`
ggplot(fd4) + geom_violin(aes(x = chosenItem, y = fpt)) + geom_point(aes(x = chosenItem, y = fpt)) +
    ylab("Mean fixations per trial") + facet_wrap(. ~ condition)

ggplot(fd4) + geom_violin(aes(x = chosenItem, y = cfpt, color = fixItem)) + 
    geom_point(aes(x = chosenItem, y = cfpt, color = fixItem), position = position_dodge(width = 0.85)) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ylim(0, 2.5) + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).
## Warning: Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Warning: Removed 1 rows containing missing values (`geom_point()`).

Stimulus-based fixation frequency (confidence considered)

dat %>%
    distinct(subj, condition, conf, trial, nFix, nFix_target) %>%
    group_by(subj, condition, conf) %>%
    summarise(fpt = mean(nFix), tfpt = mean(nFix_target)) %>%
    ungroup(subj, condition, conf) -> fd5
## `summarise()` has grouped output by 'subj', 'condition'. You can override using
## the `.groups` argument.
# total fixations
ggplot(fd5) + geom_violin(aes(x = factor(conf), y = fpt)) + geom_point(aes(x = factor(conf), y = fpt)) +
    ylab("Mean fixations per trial") + facet_wrap(. ~ condition)

# total fixations
ggplot(fd5) + geom_violin(aes(x = factor(conf), y = tfpt)) + geom_point(aes(x = factor(conf), y = tfpt)) +
    ylab("Mean target fixations per trial") + facet_wrap(. ~ condition)

Stimulus-based fixation frequency (choice and confidence considered)

dat %>%
    distinct(subj, condition, chosenItem, conf, trial, nFix, nFix_target) %>%
    group_by(subj, condition, chosenItem, conf) %>%
    summarise(fpt = mean(nFix), tfpt = mean(nFix_target)) %>%
    ungroup(subj, condition, chosenItem, conf) %>%
    complete(subj, condition, chosenItem, conf) -> fd6
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
fd6$fpt[is.na(fd6$fpt)] <- 0
fd6$tfpt[is.na(fd6$tfpt)] <- 0

# total fixations
ggplot(fd6) + geom_violin(aes(x = factor(conf), y = fpt, color = chosenItem)) + 
    geom_point(aes(x = factor(conf), y = fpt, color = chosenItem), position = position_dodge(width = 0.85)) +
    ylab("Mean fixations per trial") + facet_wrap(. ~ condition)

# total target fixations
ggplot(fd6) + geom_violin(aes(x = factor(conf), y = tfpt, color = chosenItem)) + 
    geom_point(aes(x = factor(conf), y = tfpt, color = chosenItem), position = position_dodge(width = 0.85)) +
    ylab("Mean target fixations per trial") + ylim(0, 2.5) + facet_wrap(. ~ condition)
## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

fixation dynamics

p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
    dat %>% filter(event == "fixation" & countFix <= i) %>%
        group_by(subj, condition) %>%
        mutate(totalFix = n()) %>%
        group_by(subj, fixItem, condition) %>%
        mutate(fix = n(), pFix = n()/totalFix) %>%
        select(subj, fixItem, fix, totalFix, pFix, condition) -> df
    df$i <- i
    print(distinct(df))
}

p_dat %>%
    ungroup(subj, fixItem, i) %>%
    complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0

ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
    stat_summary(fun.y = "mean", geom = "line", position = position_dodge(width = .9)) +
    scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) +
    ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## Warning: Removed 240 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 240 rows containing missing values (`geom_point()`).

fixation dynamics (exclude other fixations)

p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
    dat %>% filter(event == "fixation" & fixItem != "other" & countFix <= i) %>%
        group_by(subj, condition) %>%
        mutate(totalFix = n()) %>%
        group_by(subj, fixItem, condition) %>%
        mutate(fix = n(), pFix = n()/totalFix) %>%
        select(subj, fixItem, fix, totalFix, pFix, condition) -> df
    df$i <- i
    print(distinct(df))
}

p_dat %>%
    ungroup(subj, fixItem, i) %>%
    complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0
p_dat <- subset(p_dat, p_dat$fixItem != "other")

ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) + ylim(0, 0.7) + 
    xlab("countFix") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: Removed 180 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 180 rows containing missing values (`geom_point()`).

fixation dynamics (exclude other and dud fixations)

p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
    dat %>% filter(event == "fixation" & fixItem != "other" & fixItem != "dud" & countFix <= i) %>%
        group_by(subj, condition) %>%
        mutate(totalFix = n()) %>%
        group_by(subj, fixItem, condition) %>%
        mutate(fix = n(), pFix = n()/totalFix) %>%
        select(subj, fixItem, fix, totalFix, pFix, condition) -> df
    df$i <- i
    print(distinct(df))
}

p_dat %>%
    ungroup(subj, fixItem, i) %>%
    complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0
p_dat <- subset(p_dat, p_dat$fixItem != "other" & p_dat$fixItem != "dud")

ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) + ylim(0, 0.7) + 
    xlab("countFix") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: Removed 120 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 120 rows containing missing values (`geom_point()`).

ggplot(subset(p_dat, p_dat$i != 1), aes(x = as.numeric(as.character(condition)), y = pFix, color = fixItem)) + geom_point() +
    stat_summary(fun.y = "mean", geom = "line") +
    xlab("Condition") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ i)

nFix_target, nFix_distractorの両者で反応正誤を説明

hist(dat$nFix_target)

hist(dat$nFix_distractor)

cor(dat$nFix_target, dat$nFix_distractor)
## [1] 0.2119962
# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
       aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 0.3)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5))

f1 <- glm(corr ~ nFix_target * nFix_distractor, family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f1)
## 
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor, family = binomial, 
##     data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##         3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5899   0.4089   0.7120   0.7120   0.9806  
## 
## Coefficients:
##                             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                  0.72589    0.03337  21.750  < 2e-16 ***
## nFix_target                  0.86412    0.03003  28.776  < 2e-16 ***
## nFix_distractor             -0.08118    0.02710  -2.995  0.00274 ** 
## nFix_target:nFix_distractor -0.26585    0.02059 -12.914  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63801  on 61280  degrees of freedom
## AIC: 63809
## 
## Number of Fisher Scoring iterations: 4
Anova(f1)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                             LR Chisq Df Pr(>Chisq)    
## nFix_target                  1180.02  1  < 2.2e-16 ***
## nFix_distractor               675.89  1  < 2.2e-16 ***
## nFix_target:nFix_distractor   160.40  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f1, terms = c("nFix_target", "nFix_distractor")))

f2 <- glm(corr ~ nFix_target * factor(nFix_distractor), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f2)
## 
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor), family = binomial, 
##     data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##         3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7172   0.4100   0.7277   0.7277   0.9456  
## 
## Coefficients:
##                                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           0.79545    0.04457  17.847  < 2e-16 ***
## nFix_target                           0.95693    0.04875  19.628  < 2e-16 ***
## factor(nFix_distractor)1             -0.22238    0.05058  -4.396 1.10e-05 ***
## factor(nFix_distractor)2             -0.15117    0.06669  -2.267   0.0234 *  
## factor(nFix_distractor)3              0.26621    0.12658   2.103   0.0354 *  
## nFix_target:factor(nFix_distractor)1 -0.33656    0.05305  -6.344 2.23e-10 ***
## nFix_target:factor(nFix_distractor)2 -0.70130    0.05951 -11.785  < 2e-16 ***
## nFix_target:factor(nFix_distractor)3 -1.06525    0.08480 -12.562  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63697  on 61276  degrees of freedom
## AIC: 63713
## 
## Number of Fisher Scoring iterations: 4
Anova(f2)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                     LR Chisq Df Pr(>Chisq)    
## nFix_target                          1177.35  1  < 2.2e-16 ***
## factor(nFix_distractor)               700.84  3  < 2.2e-16 ***
## nFix_target:factor(nFix_distractor)   239.73  3  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f2, terms = c("nFix_target", "nFix_distractor")))

# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
       aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 1.2)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ condition)
## Warning: `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals

f3 <- glm(corr ~ nFix_target * nFix_distractor * factor(condition), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f3)
## 
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor * factor(condition), 
##     family = binomial, data = subset(dat, dat$nFix_target <= 
##         3 & dat$nFix_distractor <= 3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4401   0.4354   0.6665   0.7131   1.1779  
## 
## Coefficients:
##                                                    Estimate Std. Error z value
## (Intercept)                                        0.658479   0.099158   6.641
## nFix_target                                        0.887061   0.083207  10.661
## nFix_distractor                                   -0.018923   0.073250  -0.258
## factor(condition)0.3                               0.177896   0.134519   1.322
## factor(condition)0.5                               0.057240   0.130903   0.437
## factor(condition)0.7                               0.308028   0.130683   2.357
## factor(condition)0.85                              0.147753   0.123500   1.196
## factor(condition)0.95                             -0.193565   0.121881  -1.588
## nFix_target:nFix_distractor                       -0.287192   0.053060  -5.413
## nFix_target:factor(condition)0.3                  -0.289499   0.113644  -2.547
## nFix_target:factor(condition)0.5                   0.026534   0.111489   0.238
## nFix_target:factor(condition)0.7                   0.092034   0.117024   0.786
## nFix_target:factor(condition)0.85                 -0.000768   0.108116  -0.007
## nFix_target:factor(condition)0.95                  0.034725   0.106391   0.326
## nFix_distractor:factor(condition)0.3               0.095380   0.103485   0.922
## nFix_distractor:factor(condition)0.5               0.071478   0.099036   0.722
## nFix_distractor:factor(condition)0.7              -0.303614   0.102626  -2.958
## nFix_distractor:factor(condition)0.85             -0.214016   0.096263  -2.223
## nFix_distractor:factor(condition)0.95             -0.085643   0.095616  -0.896
## nFix_target:nFix_distractor:factor(condition)0.3   0.039630   0.076673   0.517
## nFix_target:nFix_distractor:factor(condition)0.5  -0.069568   0.070854  -0.982
## nFix_target:nFix_distractor:factor(condition)0.7   0.055615   0.080393   0.692
## nFix_target:nFix_distractor:factor(condition)0.85  0.093867   0.071281   1.317
## nFix_target:nFix_distractor:factor(condition)0.95  0.013025   0.071989   0.181
##                                                   Pr(>|z|)    
## (Intercept)                                       3.12e-11 ***
## nFix_target                                        < 2e-16 ***
## nFix_distractor                                    0.79615    
## factor(condition)0.3                               0.18602    
## factor(condition)0.5                               0.66192    
## factor(condition)0.7                               0.01842 *  
## factor(condition)0.85                              0.23155    
## factor(condition)0.95                              0.11225    
## nFix_target:nFix_distractor                       6.21e-08 ***
## nFix_target:factor(condition)0.3                   0.01085 *  
## nFix_target:factor(condition)0.5                   0.81188    
## nFix_target:factor(condition)0.7                   0.43160    
## nFix_target:factor(condition)0.85                  0.99433    
## nFix_target:factor(condition)0.95                  0.74413    
## nFix_distractor:factor(condition)0.3               0.35669    
## nFix_distractor:factor(condition)0.5               0.47045    
## nFix_distractor:factor(condition)0.7               0.00309 ** 
## nFix_distractor:factor(condition)0.85              0.02620 *  
## nFix_distractor:factor(condition)0.95              0.37042    
## nFix_target:nFix_distractor:factor(condition)0.3   0.60525    
## nFix_target:nFix_distractor:factor(condition)0.5   0.32617    
## nFix_target:nFix_distractor:factor(condition)0.7   0.48907    
## nFix_target:nFix_distractor:factor(condition)0.85  0.18788    
## nFix_target:nFix_distractor:factor(condition)0.95  0.85643    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63544  on 61260  degrees of freedom
## AIC: 63592
## 
## Number of Fisher Scoring iterations: 4
Anova(f3)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                               LR Chisq Df Pr(>Chisq)    
## nFix_target                                    1171.00  1  < 2.2e-16 ***
## nFix_distractor                                 702.91  1  < 2.2e-16 ***
## factor(condition)                               149.69  5  < 2.2e-16 ***
## nFix_target:nFix_distractor                     157.34  1  < 2.2e-16 ***
## nFix_target:factor(condition)                    62.41  5  3.850e-12 ***
## nFix_distractor:factor(condition)                61.99  5  4.721e-12 ***
## nFix_target:nFix_distractor:factor(condition)     6.69  5     0.2446    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f3, terms = c("nFix_target", "nFix_distractor", "condition")))

f4 <- glm(corr ~ nFix_target * factor(nFix_distractor) * factor(condition), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f4)
## 
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor) * 
##     factor(condition), family = binomial, data = subset(dat, 
##     dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5447   0.4290   0.6893   0.7247   1.2932  
## 
## Coefficients:
##                                                            Estimate Std. Error
## (Intercept)                                                 0.41093    0.15573
## nFix_target                                                 1.26856    0.16683
## factor(nFix_distractor)1                                    0.28644    0.16802
## factor(nFix_distractor)2                                    0.15488    0.19787
## factor(nFix_distractor)3                                    0.06497    0.29110
## factor(condition)0.3                                        0.47884    0.20177
## factor(condition)0.5                                        0.48312    0.19856
## factor(condition)0.7                                        0.68899    0.19078
## factor(condition)0.85                                       0.43051    0.18056
## factor(condition)0.95                                       0.13439    0.17913
## nFix_target:factor(nFix_distractor)1                       -0.76305    0.17473
## nFix_target:factor(nFix_distractor)2                       -0.80693    0.18784
## nFix_target:factor(nFix_distractor)3                       -1.27376    0.21991
## nFix_target:factor(condition)0.3                           -0.60679    0.21215
## nFix_target:factor(condition)0.5                           -0.20906    0.21554
## nFix_target:factor(condition)0.7                           -0.21967    0.21032
## nFix_target:factor(condition)0.85                          -0.26929    0.19669
## nFix_target:factor(condition)0.95                          -0.31501    0.19210
## factor(nFix_distractor)1:factor(condition)0.3              -0.27102    0.21968
## factor(nFix_distractor)2:factor(condition)0.3              -0.37933    0.26631
## factor(nFix_distractor)3:factor(condition)0.3               1.77629    0.47861
## factor(nFix_distractor)1:factor(condition)0.5              -0.57400    0.21699
## factor(nFix_distractor)2:factor(condition)0.5               0.06508    0.26475
## factor(nFix_distractor)3:factor(condition)0.5               0.75189    0.43028
## factor(nFix_distractor)1:factor(condition)0.7              -0.86121    0.20929
## factor(nFix_distractor)2:factor(condition)0.7              -0.56123    0.25930
## factor(nFix_distractor)3:factor(condition)0.7              -1.43292    0.48089
## factor(nFix_distractor)1:factor(condition)0.85             -0.64135    0.20008
## factor(nFix_distractor)2:factor(condition)0.85             -0.46932    0.24889
## factor(nFix_distractor)3:factor(condition)0.85              0.38594    0.49340
## factor(nFix_distractor)1:factor(condition)0.95             -0.55699    0.19718
## factor(nFix_distractor)2:factor(condition)0.95             -0.37748    0.24590
## factor(nFix_distractor)3:factor(condition)0.95              0.50057    0.44713
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3   0.44626    0.22384
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3   0.39322    0.24543
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3  -0.28195    0.32865
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5   0.33407    0.22794
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5  -0.27669    0.24614
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5  -0.16698    0.29789
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7   0.50525    0.22325
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7   0.07794    0.24298
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7   0.77350    0.37892
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85  0.51773    0.21039
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85  0.03628    0.23058
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85  0.46791    0.32060
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95  0.49770    0.20456
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95  0.11430    0.22493
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95  0.14622    0.31274
##                                                            z value Pr(>|z|)    
## (Intercept)                                                  2.639 0.008323 ** 
## nFix_target                                                  7.604 2.87e-14 ***
## factor(nFix_distractor)1                                     1.705 0.088239 .  
## factor(nFix_distractor)2                                     0.783 0.433773    
## factor(nFix_distractor)3                                     0.223 0.823384    
## factor(condition)0.3                                         2.373 0.017634 *  
## factor(condition)0.5                                         2.433 0.014970 *  
## factor(condition)0.7                                         3.611 0.000305 ***
## factor(condition)0.85                                        2.384 0.017110 *  
## factor(condition)0.95                                        0.750 0.453124    
## nFix_target:factor(nFix_distractor)1                        -4.367 1.26e-05 ***
## nFix_target:factor(nFix_distractor)2                        -4.296 1.74e-05 ***
## nFix_target:factor(nFix_distractor)3                        -5.792 6.95e-09 ***
## nFix_target:factor(condition)0.3                            -2.860 0.004234 ** 
## nFix_target:factor(condition)0.5                            -0.970 0.332076    
## nFix_target:factor(condition)0.7                            -1.044 0.296283    
## nFix_target:factor(condition)0.85                           -1.369 0.170966    
## nFix_target:factor(condition)0.95                           -1.640 0.101041    
## factor(nFix_distractor)1:factor(condition)0.3               -1.234 0.217311    
## factor(nFix_distractor)2:factor(condition)0.3               -1.424 0.154331    
## factor(nFix_distractor)3:factor(condition)0.3                3.711 0.000206 ***
## factor(nFix_distractor)1:factor(condition)0.5               -2.645 0.008161 ** 
## factor(nFix_distractor)2:factor(condition)0.5                0.246 0.805819    
## factor(nFix_distractor)3:factor(condition)0.5                1.747 0.080560 .  
## factor(nFix_distractor)1:factor(condition)0.7               -4.115 3.87e-05 ***
## factor(nFix_distractor)2:factor(condition)0.7               -2.164 0.030431 *  
## factor(nFix_distractor)3:factor(condition)0.7               -2.980 0.002885 ** 
## factor(nFix_distractor)1:factor(condition)0.85              -3.206 0.001348 ** 
## factor(nFix_distractor)2:factor(condition)0.85              -1.886 0.059340 .  
## factor(nFix_distractor)3:factor(condition)0.85               0.782 0.434100    
## factor(nFix_distractor)1:factor(condition)0.95              -2.825 0.004732 ** 
## factor(nFix_distractor)2:factor(condition)0.95              -1.535 0.124760    
## factor(nFix_distractor)3:factor(condition)0.95               1.120 0.262922    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3    1.994 0.046190 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3    1.602 0.109127    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3   -0.858 0.390947    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5    1.466 0.142757    
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5   -1.124 0.260952    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5   -0.561 0.575122    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7    2.263 0.023624 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7    0.321 0.748404    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7    2.041 0.041222 *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85   2.461 0.013862 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85   0.157 0.874968    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85   1.459 0.144435    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95   2.433 0.014971 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95   0.508 0.611334    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95   0.468 0.640103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63304  on 61236  degrees of freedom
## AIC: 63400
## 
## Number of Fisher Scoring iterations: 4
Anova(f4)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                                       LR Chisq Df Pr(>Chisq)
## nFix_target                                            1158.69  1  < 2.2e-16
## factor(nFix_distractor)                                 734.61  3  < 2.2e-16
## factor(condition)                                       158.19  5  < 2.2e-16
## nFix_target:factor(nFix_distractor)                     245.73  3  < 2.2e-16
## nFix_target:factor(condition)                            56.67  5  5.919e-11
## factor(nFix_distractor):factor(condition)               144.56 15  < 2.2e-16
## nFix_target:factor(nFix_distractor):factor(condition)    51.13 15  7.865e-06
##                                                          
## nFix_target                                           ***
## factor(nFix_distractor)                               ***
## factor(condition)                                     ***
## nFix_target:factor(nFix_distractor)                   ***
## nFix_target:factor(condition)                         ***
## factor(nFix_distractor):factor(condition)             ***
## nFix_target:factor(nFix_distractor):factor(condition) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f4, terms = c("nFix_target", "nFix_distractor", "condition")))

nFix_target, nFix_distractorの両者で確信度を説明

hist(dat$nFix_target)

hist(dat$nFix_distractor)

hist(dat$conf)

# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
       aes(x = nFix_target, , y = conf, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 0.3)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ chosenItem)

f1 <- glm(corr ~ nFix_target * nFix_distractor, family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f1)
## 
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor, family = binomial, 
##     data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##         3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5899   0.4089   0.7120   0.7120   0.9806  
## 
## Coefficients:
##                             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                  0.72589    0.03337  21.750  < 2e-16 ***
## nFix_target                  0.86412    0.03003  28.776  < 2e-16 ***
## nFix_distractor             -0.08118    0.02710  -2.995  0.00274 ** 
## nFix_target:nFix_distractor -0.26585    0.02059 -12.914  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63801  on 61280  degrees of freedom
## AIC: 63809
## 
## Number of Fisher Scoring iterations: 4
Anova(f1)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                             LR Chisq Df Pr(>Chisq)    
## nFix_target                  1180.02  1  < 2.2e-16 ***
## nFix_distractor               675.89  1  < 2.2e-16 ***
## nFix_target:nFix_distractor   160.40  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f1, terms = c("nFix_target", "nFix_distractor")))

f2 <- glm(corr ~ nFix_target * factor(nFix_distractor), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f2)
## 
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor), family = binomial, 
##     data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##         3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7172   0.4100   0.7277   0.7277   0.9456  
## 
## Coefficients:
##                                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           0.79545    0.04457  17.847  < 2e-16 ***
## nFix_target                           0.95693    0.04875  19.628  < 2e-16 ***
## factor(nFix_distractor)1             -0.22238    0.05058  -4.396 1.10e-05 ***
## factor(nFix_distractor)2             -0.15117    0.06669  -2.267   0.0234 *  
## factor(nFix_distractor)3              0.26621    0.12658   2.103   0.0354 *  
## nFix_target:factor(nFix_distractor)1 -0.33656    0.05305  -6.344 2.23e-10 ***
## nFix_target:factor(nFix_distractor)2 -0.70130    0.05951 -11.785  < 2e-16 ***
## nFix_target:factor(nFix_distractor)3 -1.06525    0.08480 -12.562  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63697  on 61276  degrees of freedom
## AIC: 63713
## 
## Number of Fisher Scoring iterations: 4
Anova(f2)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                     LR Chisq Df Pr(>Chisq)    
## nFix_target                          1177.35  1  < 2.2e-16 ***
## factor(nFix_distractor)               700.84  3  < 2.2e-16 ***
## nFix_target:factor(nFix_distractor)   239.73  3  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f2, terms = c("nFix_target", "nFix_distractor")))

# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
       aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 1.2)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ condition)
## Warning: `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals

f3 <- glm(corr ~ nFix_target * nFix_distractor * factor(condition), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f3)
## 
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor * factor(condition), 
##     family = binomial, data = subset(dat, dat$nFix_target <= 
##         3 & dat$nFix_distractor <= 3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4401   0.4354   0.6665   0.7131   1.1779  
## 
## Coefficients:
##                                                    Estimate Std. Error z value
## (Intercept)                                        0.658479   0.099158   6.641
## nFix_target                                        0.887061   0.083207  10.661
## nFix_distractor                                   -0.018923   0.073250  -0.258
## factor(condition)0.3                               0.177896   0.134519   1.322
## factor(condition)0.5                               0.057240   0.130903   0.437
## factor(condition)0.7                               0.308028   0.130683   2.357
## factor(condition)0.85                              0.147753   0.123500   1.196
## factor(condition)0.95                             -0.193565   0.121881  -1.588
## nFix_target:nFix_distractor                       -0.287192   0.053060  -5.413
## nFix_target:factor(condition)0.3                  -0.289499   0.113644  -2.547
## nFix_target:factor(condition)0.5                   0.026534   0.111489   0.238
## nFix_target:factor(condition)0.7                   0.092034   0.117024   0.786
## nFix_target:factor(condition)0.85                 -0.000768   0.108116  -0.007
## nFix_target:factor(condition)0.95                  0.034725   0.106391   0.326
## nFix_distractor:factor(condition)0.3               0.095380   0.103485   0.922
## nFix_distractor:factor(condition)0.5               0.071478   0.099036   0.722
## nFix_distractor:factor(condition)0.7              -0.303614   0.102626  -2.958
## nFix_distractor:factor(condition)0.85             -0.214016   0.096263  -2.223
## nFix_distractor:factor(condition)0.95             -0.085643   0.095616  -0.896
## nFix_target:nFix_distractor:factor(condition)0.3   0.039630   0.076673   0.517
## nFix_target:nFix_distractor:factor(condition)0.5  -0.069568   0.070854  -0.982
## nFix_target:nFix_distractor:factor(condition)0.7   0.055615   0.080393   0.692
## nFix_target:nFix_distractor:factor(condition)0.85  0.093867   0.071281   1.317
## nFix_target:nFix_distractor:factor(condition)0.95  0.013025   0.071989   0.181
##                                                   Pr(>|z|)    
## (Intercept)                                       3.12e-11 ***
## nFix_target                                        < 2e-16 ***
## nFix_distractor                                    0.79615    
## factor(condition)0.3                               0.18602    
## factor(condition)0.5                               0.66192    
## factor(condition)0.7                               0.01842 *  
## factor(condition)0.85                              0.23155    
## factor(condition)0.95                              0.11225    
## nFix_target:nFix_distractor                       6.21e-08 ***
## nFix_target:factor(condition)0.3                   0.01085 *  
## nFix_target:factor(condition)0.5                   0.81188    
## nFix_target:factor(condition)0.7                   0.43160    
## nFix_target:factor(condition)0.85                  0.99433    
## nFix_target:factor(condition)0.95                  0.74413    
## nFix_distractor:factor(condition)0.3               0.35669    
## nFix_distractor:factor(condition)0.5               0.47045    
## nFix_distractor:factor(condition)0.7               0.00309 ** 
## nFix_distractor:factor(condition)0.85              0.02620 *  
## nFix_distractor:factor(condition)0.95              0.37042    
## nFix_target:nFix_distractor:factor(condition)0.3   0.60525    
## nFix_target:nFix_distractor:factor(condition)0.5   0.32617    
## nFix_target:nFix_distractor:factor(condition)0.7   0.48907    
## nFix_target:nFix_distractor:factor(condition)0.85  0.18788    
## nFix_target:nFix_distractor:factor(condition)0.95  0.85643    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63544  on 61260  degrees of freedom
## AIC: 63592
## 
## Number of Fisher Scoring iterations: 4
Anova(f3)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                               LR Chisq Df Pr(>Chisq)    
## nFix_target                                    1171.00  1  < 2.2e-16 ***
## nFix_distractor                                 702.91  1  < 2.2e-16 ***
## factor(condition)                               149.69  5  < 2.2e-16 ***
## nFix_target:nFix_distractor                     157.34  1  < 2.2e-16 ***
## nFix_target:factor(condition)                    62.41  5  3.850e-12 ***
## nFix_distractor:factor(condition)                61.99  5  4.721e-12 ***
## nFix_target:nFix_distractor:factor(condition)     6.69  5     0.2446    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f3, terms = c("nFix_target", "nFix_distractor", "condition")))

f4 <- glm(corr ~ nFix_target * factor(nFix_distractor) * factor(condition), family = binomial, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f4)
## 
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor) * 
##     factor(condition), family = binomial, data = subset(dat, 
##     dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5447   0.4290   0.6893   0.7247   1.2932  
## 
## Coefficients:
##                                                            Estimate Std. Error
## (Intercept)                                                 0.41093    0.15573
## nFix_target                                                 1.26856    0.16683
## factor(nFix_distractor)1                                    0.28644    0.16802
## factor(nFix_distractor)2                                    0.15488    0.19787
## factor(nFix_distractor)3                                    0.06497    0.29110
## factor(condition)0.3                                        0.47884    0.20177
## factor(condition)0.5                                        0.48312    0.19856
## factor(condition)0.7                                        0.68899    0.19078
## factor(condition)0.85                                       0.43051    0.18056
## factor(condition)0.95                                       0.13439    0.17913
## nFix_target:factor(nFix_distractor)1                       -0.76305    0.17473
## nFix_target:factor(nFix_distractor)2                       -0.80693    0.18784
## nFix_target:factor(nFix_distractor)3                       -1.27376    0.21991
## nFix_target:factor(condition)0.3                           -0.60679    0.21215
## nFix_target:factor(condition)0.5                           -0.20906    0.21554
## nFix_target:factor(condition)0.7                           -0.21967    0.21032
## nFix_target:factor(condition)0.85                          -0.26929    0.19669
## nFix_target:factor(condition)0.95                          -0.31501    0.19210
## factor(nFix_distractor)1:factor(condition)0.3              -0.27102    0.21968
## factor(nFix_distractor)2:factor(condition)0.3              -0.37933    0.26631
## factor(nFix_distractor)3:factor(condition)0.3               1.77629    0.47861
## factor(nFix_distractor)1:factor(condition)0.5              -0.57400    0.21699
## factor(nFix_distractor)2:factor(condition)0.5               0.06508    0.26475
## factor(nFix_distractor)3:factor(condition)0.5               0.75189    0.43028
## factor(nFix_distractor)1:factor(condition)0.7              -0.86121    0.20929
## factor(nFix_distractor)2:factor(condition)0.7              -0.56123    0.25930
## factor(nFix_distractor)3:factor(condition)0.7              -1.43292    0.48089
## factor(nFix_distractor)1:factor(condition)0.85             -0.64135    0.20008
## factor(nFix_distractor)2:factor(condition)0.85             -0.46932    0.24889
## factor(nFix_distractor)3:factor(condition)0.85              0.38594    0.49340
## factor(nFix_distractor)1:factor(condition)0.95             -0.55699    0.19718
## factor(nFix_distractor)2:factor(condition)0.95             -0.37748    0.24590
## factor(nFix_distractor)3:factor(condition)0.95              0.50057    0.44713
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3   0.44626    0.22384
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3   0.39322    0.24543
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3  -0.28195    0.32865
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5   0.33407    0.22794
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5  -0.27669    0.24614
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5  -0.16698    0.29789
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7   0.50525    0.22325
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7   0.07794    0.24298
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7   0.77350    0.37892
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85  0.51773    0.21039
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85  0.03628    0.23058
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85  0.46791    0.32060
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95  0.49770    0.20456
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95  0.11430    0.22493
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95  0.14622    0.31274
##                                                            z value Pr(>|z|)    
## (Intercept)                                                  2.639 0.008323 ** 
## nFix_target                                                  7.604 2.87e-14 ***
## factor(nFix_distractor)1                                     1.705 0.088239 .  
## factor(nFix_distractor)2                                     0.783 0.433773    
## factor(nFix_distractor)3                                     0.223 0.823384    
## factor(condition)0.3                                         2.373 0.017634 *  
## factor(condition)0.5                                         2.433 0.014970 *  
## factor(condition)0.7                                         3.611 0.000305 ***
## factor(condition)0.85                                        2.384 0.017110 *  
## factor(condition)0.95                                        0.750 0.453124    
## nFix_target:factor(nFix_distractor)1                        -4.367 1.26e-05 ***
## nFix_target:factor(nFix_distractor)2                        -4.296 1.74e-05 ***
## nFix_target:factor(nFix_distractor)3                        -5.792 6.95e-09 ***
## nFix_target:factor(condition)0.3                            -2.860 0.004234 ** 
## nFix_target:factor(condition)0.5                            -0.970 0.332076    
## nFix_target:factor(condition)0.7                            -1.044 0.296283    
## nFix_target:factor(condition)0.85                           -1.369 0.170966    
## nFix_target:factor(condition)0.95                           -1.640 0.101041    
## factor(nFix_distractor)1:factor(condition)0.3               -1.234 0.217311    
## factor(nFix_distractor)2:factor(condition)0.3               -1.424 0.154331    
## factor(nFix_distractor)3:factor(condition)0.3                3.711 0.000206 ***
## factor(nFix_distractor)1:factor(condition)0.5               -2.645 0.008161 ** 
## factor(nFix_distractor)2:factor(condition)0.5                0.246 0.805819    
## factor(nFix_distractor)3:factor(condition)0.5                1.747 0.080560 .  
## factor(nFix_distractor)1:factor(condition)0.7               -4.115 3.87e-05 ***
## factor(nFix_distractor)2:factor(condition)0.7               -2.164 0.030431 *  
## factor(nFix_distractor)3:factor(condition)0.7               -2.980 0.002885 ** 
## factor(nFix_distractor)1:factor(condition)0.85              -3.206 0.001348 ** 
## factor(nFix_distractor)2:factor(condition)0.85              -1.886 0.059340 .  
## factor(nFix_distractor)3:factor(condition)0.85               0.782 0.434100    
## factor(nFix_distractor)1:factor(condition)0.95              -2.825 0.004732 ** 
## factor(nFix_distractor)2:factor(condition)0.95              -1.535 0.124760    
## factor(nFix_distractor)3:factor(condition)0.95               1.120 0.262922    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3    1.994 0.046190 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3    1.602 0.109127    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3   -0.858 0.390947    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5    1.466 0.142757    
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5   -1.124 0.260952    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5   -0.561 0.575122    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7    2.263 0.023624 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7    0.321 0.748404    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7    2.041 0.041222 *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85   2.461 0.013862 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85   0.157 0.874968    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85   1.459 0.144435    
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95   2.433 0.014971 *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95   0.508 0.611334    
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95   0.468 0.640103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65497  on 61283  degrees of freedom
## Residual deviance: 63304  on 61236  degrees of freedom
## AIC: 63400
## 
## Number of Fisher Scoring iterations: 4
Anova(f4)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                                       LR Chisq Df Pr(>Chisq)
## nFix_target                                            1158.69  1  < 2.2e-16
## factor(nFix_distractor)                                 734.61  3  < 2.2e-16
## factor(condition)                                       158.19  5  < 2.2e-16
## nFix_target:factor(nFix_distractor)                     245.73  3  < 2.2e-16
## nFix_target:factor(condition)                            56.67  5  5.919e-11
## factor(nFix_distractor):factor(condition)               144.56 15  < 2.2e-16
## nFix_target:factor(nFix_distractor):factor(condition)    51.13 15  7.865e-06
##                                                          
## nFix_target                                           ***
## factor(nFix_distractor)                               ***
## factor(condition)                                     ***
## nFix_target:factor(nFix_distractor)                   ***
## nFix_target:factor(condition)                         ***
## factor(nFix_distractor):factor(condition)             ***
## nFix_target:factor(nFix_distractor):factor(condition) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f4, terms = c("nFix_target", "nFix_distractor", "condition")))

nFix_target, nFix_distractorの両者で標準化された確信度を説明

hist(dat$nFix_target)

hist(dat$nFix_distractor)

hist(dat$conf_normalized)

# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & dat$subj != "sub03"), # sub03 almost always gives conf of 4
       aes(x = nFix_target, , y = conf_normalized, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 0.3)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + 
    ylim(-3, 3) + facet_wrap(. ~ chosenItem)

f5 <- lm(conf_normalized ~ nFix_target * nFix_distractor * chosenItem, 
          data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & 
                            dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f5)
## 
## Call:
## lm(formula = conf_normalized ~ nFix_target * nFix_distractor * 
##     chosenItem, data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##     3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.96575 -0.69370  0.09428  0.65711  3.04754 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                       0.10247    0.01705   6.011
## nFix_target                                       0.10629    0.01363   7.800
## nFix_distractor                                   0.05601    0.01419   3.947
## chosenItemdistractor                             -0.28743    0.03345  -8.594
## nFix_target:nFix_distractor                      -0.11409    0.01018 -11.212
## nFix_target:chosenItemdistractor                 -0.20875    0.02832  -7.371
## nFix_distractor:chosenItemdistractor             -0.13654    0.02761  -4.944
## nFix_target:nFix_distractor:chosenItemdistractor  0.09478    0.02004   4.729
##                                                  Pr(>|t|)    
## (Intercept)                                      1.86e-09 ***
## nFix_target                                      6.29e-15 ***
## nFix_distractor                                  7.93e-05 ***
## chosenItemdistractor                              < 2e-16 ***
## nFix_target:nFix_distractor                       < 2e-16 ***
## nFix_target:chosenItemdistractor                 1.71e-13 ***
## nFix_distractor:chosenItemdistractor             7.66e-07 ***
## nFix_target:nFix_distractor:chosenItemdistractor 2.27e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9639 on 54634 degrees of freedom
## Multiple R-squared:  0.06001,    Adjusted R-squared:  0.05989 
## F-statistic: 498.3 on 7 and 54634 DF,  p-value: < 2.2e-16
Anova(f5)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                        Sum Sq    Df   F value    Pr(>F)    
## nFix_target                                51     1   54.5702 1.521e-13 ***
## nFix_distractor                           186     1  200.2002 < 2.2e-16 ***
## chosenItem                               2605     1 2803.6099 < 2.2e-16 ***
## nFix_target:nFix_distractor                97     1  104.5923 < 2.2e-16 ***
## nFix_target:chosenItem                     37     1   40.1521 2.368e-10 ***
## nFix_distractor:chosenItem                  3     1    3.2241   0.07257 .  
## nFix_target:nFix_distractor:chosenItem     21     1   22.3608 2.265e-06 ***
## Residuals                               50766 54634                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f5, terms = c("nFix_target", "nFix_distractor", "chosenItem")))

f6 <- lm(conf_normalized ~ nFix_target * factor(nFix_distractor) * chosenItem, 
         data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & 
                           dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f6)
## 
## Call:
## lm(formula = conf_normalized ~ nFix_target * factor(nFix_distractor) * 
##     chosenItem, data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 
##     3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.96078 -0.66786  0.08534  0.65950  3.03392 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                0.11530    0.02185
## nFix_target                                                0.08850    0.01979
## factor(nFix_distractor)1                                   0.03325    0.02511
## factor(nFix_distractor)2                                   0.12052    0.03394
## factor(nFix_distractor)3                                   0.15332    0.06483
## chosenItemdistractor                                      -0.34318    0.04366
## nFix_target:factor(nFix_distractor)1                      -0.08096    0.02182
## nFix_target:factor(nFix_distractor)2                      -0.26097    0.02591
## nFix_target:factor(nFix_distractor)3                      -0.24987    0.04149
## nFix_target:chosenItemdistractor                          -0.19902    0.04536
## factor(nFix_distractor)1:chosenItemdistractor             -0.03788    0.04922
## factor(nFix_distractor)2:chosenItemdistractor             -0.34740    0.06532
## factor(nFix_distractor)3:chosenItemdistractor             -0.30281    0.12432
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor  0.05036    0.04913
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor  0.31387    0.05612
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor  0.13104    0.08165
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 5.278 1.31e-07 ***
## nFix_target                                                 4.471 7.80e-06 ***
## factor(nFix_distractor)1                                    1.324 0.185438    
## factor(nFix_distractor)2                                    3.551 0.000384 ***
## factor(nFix_distractor)3                                    2.365 0.018040 *  
## chosenItemdistractor                                       -7.861 3.87e-15 ***
## nFix_target:factor(nFix_distractor)1                       -3.710 0.000207 ***
## nFix_target:factor(nFix_distractor)2                      -10.071  < 2e-16 ***
## nFix_target:factor(nFix_distractor)3                       -6.022 1.73e-09 ***
## nFix_target:chosenItemdistractor                           -4.387 1.15e-05 ***
## factor(nFix_distractor)1:chosenItemdistractor              -0.769 0.441614    
## factor(nFix_distractor)2:chosenItemdistractor              -5.318 1.05e-07 ***
## factor(nFix_distractor)3:chosenItemdistractor              -2.436 0.014867 *  
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor   1.025 0.305390    
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor   5.593 2.25e-08 ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor   1.605 0.108519    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9634 on 54626 degrees of freedom
## Multiple R-squared:  0.06127,    Adjusted R-squared:  0.06101 
## F-statistic: 237.7 on 15 and 54626 DF,  p-value: < 2.2e-16
Anova(f6)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                Sum Sq    Df   F value    Pr(>F)
## nFix_target                                        50     1   53.7707 2.284e-13
## factor(nFix_distractor)                           224     3   80.3469 < 2.2e-16
## chosenItem                                       2600     1 2801.5923 < 2.2e-16
## nFix_target:factor(nFix_distractor)                87     3   31.0789 < 2.2e-16
## nFix_target:chosenItem                             38     1   40.5688 1.913e-10
## factor(nFix_distractor):chosenItem                 12     3    4.1484  0.006007
## nFix_target:factor(nFix_distractor):chosenItem     50     3   17.9418 1.238e-11
## Residuals                                       50698 54626                    
##                                                   
## nFix_target                                    ***
## factor(nFix_distractor)                        ***
## chosenItem                                     ***
## nFix_target:factor(nFix_distractor)            ***
## nFix_target:chosenItem                         ***
## factor(nFix_distractor):chosenItem             ** 
## nFix_target:factor(nFix_distractor):chosenItem ***
## Residuals                                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f6, terms = c("nFix_target", "nFix_distractor", "chosenItem")))

# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & 
                  dat$chosenItem != "dud" & dat$subj != "sub03"), # sub03 almost always gives conf of 4
       aes(x = nFix_target, , y = conf_normalized, color = factor(nFix_distractor))) + 
    geom_count(position = position_dodge(width = 0.3)) +
    stat_summary(fun.y = "mean", geom = "line") +
    scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + 
    ylim(-3, 3) + facet_nested(. ~ chosenItem + condition)

f7 <- lm(conf_normalized ~ nFix_target * nFix_distractor * chosenItem * factor(condition), 
         data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & 
                           dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f7)
## 
## Call:
## lm(formula = conf_normalized ~ nFix_target * nFix_distractor * 
##     chosenItem * factor(condition), data = subset(dat, dat$nFix_target <= 
##     3 & dat$nFix_distractor <= 3 & dat$chosenItem != "dud" & 
##     dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1010 -0.6931  0.1139  0.7333  3.3163 
## 
## Coefficients:
##                                                                          Estimate
## (Intercept)                                                             0.0752960
## nFix_target                                                             0.1913408
## nFix_distractor                                                         0.0608421
## chosenItemdistractor                                                   -0.3104762
## factor(condition)0.3                                                    0.3251223
## factor(condition)0.5                                                    0.2776974
## factor(condition)0.7                                                    0.1232229
## factor(condition)0.85                                                  -0.0779113
## factor(condition)0.95                                                  -0.2993420
## nFix_target:nFix_distractor                                            -0.1781547
## nFix_target:chosenItemdistractor                                       -0.3944236
## nFix_distractor:chosenItemdistractor                                   -0.0930693
## nFix_target:factor(condition)0.3                                       -0.2165153
## nFix_target:factor(condition)0.5                                       -0.2086613
## nFix_target:factor(condition)0.7                                       -0.1034811
## nFix_target:factor(condition)0.85                                      -0.0547068
## nFix_target:factor(condition)0.95                                       0.0153728
## nFix_distractor:factor(condition)0.3                                   -0.0104797
## nFix_distractor:factor(condition)0.5                                   -0.0434750
## nFix_distractor:factor(condition)0.7                                    0.0001316
## nFix_distractor:factor(condition)0.85                                  -0.0716915
## nFix_distractor:factor(condition)0.95                                  -0.0709066
## chosenItemdistractor:factor(condition)0.3                               0.0511489
## chosenItemdistractor:factor(condition)0.5                              -0.0756553
## chosenItemdistractor:factor(condition)0.7                               0.1099306
## chosenItemdistractor:factor(condition)0.85                             -0.1536403
## chosenItemdistractor:factor(condition)0.95                              0.1240766
## nFix_target:nFix_distractor:chosenItemdistractor                        0.1372286
## nFix_target:nFix_distractor:factor(condition)0.3                        0.0965452
## nFix_target:nFix_distractor:factor(condition)0.5                        0.1277425
## nFix_target:nFix_distractor:factor(condition)0.7                        0.0407039
## nFix_target:nFix_distractor:factor(condition)0.85                       0.1053057
## nFix_target:nFix_distractor:factor(condition)0.95                       0.0507342
## nFix_target:chosenItemdistractor:factor(condition)0.3                   0.3294910
## nFix_target:chosenItemdistractor:factor(condition)0.5                   0.2720565
## nFix_target:chosenItemdistractor:factor(condition)0.7                   0.1935701
## nFix_target:chosenItemdistractor:factor(condition)0.85                  0.2433877
## nFix_target:chosenItemdistractor:factor(condition)0.95                  0.0011849
## nFix_distractor:chosenItemdistractor:factor(condition)0.3              -0.0841418
## nFix_distractor:chosenItemdistractor:factor(condition)0.5              -0.0361155
## nFix_distractor:chosenItemdistractor:factor(condition)0.7              -0.1542726
## nFix_distractor:chosenItemdistractor:factor(condition)0.85              0.1214649
## nFix_distractor:chosenItemdistractor:factor(condition)0.95              0.0177608
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3  -0.1111535
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5  -0.0594830
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7   0.0539932
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 -0.1343595
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95  0.0165684
##                                                                        Std. Error
## (Intercept)                                                             0.0487084
## nFix_target                                                             0.0363066
## nFix_distractor                                                         0.0371688
## chosenItemdistractor                                                    0.0909827
## factor(condition)0.3                                                    0.0665190
## factor(condition)0.5                                                    0.0644115
## factor(condition)0.7                                                    0.0629589
## factor(condition)0.85                                                   0.0610640
## factor(condition)0.95                                                   0.0620366
## nFix_target:nFix_distractor                                             0.0253124
## nFix_target:chosenItemdistractor                                        0.0737198
## nFix_distractor:chosenItemdistractor                                    0.0695153
## nFix_target:factor(condition)0.3                                        0.0514115
## nFix_target:factor(condition)0.5                                        0.0490580
## nFix_target:factor(condition)0.7                                        0.0492718
## nFix_target:factor(condition)0.85                                       0.0468097
## nFix_target:factor(condition)0.95                                       0.0481968
## nFix_distractor:factor(condition)0.3                                    0.0522441
## nFix_distractor:factor(condition)0.5                                    0.0500810
## nFix_distractor:factor(condition)0.7                                    0.0513809
## nFix_distractor:factor(condition)0.85                                   0.0489418
## nFix_distractor:factor(condition)0.95                                   0.0504289
## chosenItemdistractor:factor(condition)0.3                               0.1240920
## chosenItemdistractor:factor(condition)0.5                               0.1222557
## chosenItemdistractor:factor(condition)0.7                               0.1202176
## chosenItemdistractor:factor(condition)0.85                              0.1172860
## chosenItemdistractor:factor(condition)0.95                              0.1214964
## nFix_target:nFix_distractor:chosenItemdistractor                        0.0491510
## nFix_target:nFix_distractor:factor(condition)0.3                        0.0374508
## nFix_target:nFix_distractor:factor(condition)0.5                        0.0343778
## nFix_target:nFix_distractor:factor(condition)0.7                        0.0368516
## nFix_target:nFix_distractor:factor(condition)0.85                       0.0334941
## nFix_target:nFix_distractor:factor(condition)0.95                       0.0357016
## nFix_target:chosenItemdistractor:factor(condition)0.3                   0.1002732
## nFix_target:chosenItemdistractor:factor(condition)0.5                   0.0989884
## nFix_target:chosenItemdistractor:factor(condition)0.7                   0.1033299
## nFix_target:chosenItemdistractor:factor(condition)0.85                  0.0995152
## nFix_target:chosenItemdistractor:factor(condition)0.95                  0.1014336
## nFix_distractor:chosenItemdistractor:factor(condition)0.3               0.0978450
## nFix_distractor:chosenItemdistractor:factor(condition)0.5               0.0963152
## nFix_distractor:chosenItemdistractor:factor(condition)0.7               0.0971070
## nFix_distractor:chosenItemdistractor:factor(condition)0.85              0.0944060
## nFix_distractor:chosenItemdistractor:factor(condition)0.95              0.0972364
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3   0.0695927
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5   0.0660706
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7   0.0738962
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85  0.0688887
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95  0.0705294
##                                                                        t value
## (Intercept)                                                              1.546
## nFix_target                                                              5.270
## nFix_distractor                                                          1.637
## chosenItemdistractor                                                    -3.412
## factor(condition)0.3                                                     4.888
## factor(condition)0.5                                                     4.311
## factor(condition)0.7                                                     1.957
## factor(condition)0.85                                                   -1.276
## factor(condition)0.95                                                   -4.825
## nFix_target:nFix_distractor                                             -7.038
## nFix_target:chosenItemdistractor                                        -5.350
## nFix_distractor:chosenItemdistractor                                    -1.339
## nFix_target:factor(condition)0.3                                        -4.211
## nFix_target:factor(condition)0.5                                        -4.253
## nFix_target:factor(condition)0.7                                        -2.100
## nFix_target:factor(condition)0.85                                       -1.169
## nFix_target:factor(condition)0.95                                        0.319
## nFix_distractor:factor(condition)0.3                                    -0.201
## nFix_distractor:factor(condition)0.5                                    -0.868
## nFix_distractor:factor(condition)0.7                                     0.003
## nFix_distractor:factor(condition)0.85                                   -1.465
## nFix_distractor:factor(condition)0.95                                   -1.406
## chosenItemdistractor:factor(condition)0.3                                0.412
## chosenItemdistractor:factor(condition)0.5                               -0.619
## chosenItemdistractor:factor(condition)0.7                                0.914
## chosenItemdistractor:factor(condition)0.85                              -1.310
## chosenItemdistractor:factor(condition)0.95                               1.021
## nFix_target:nFix_distractor:chosenItemdistractor                         2.792
## nFix_target:nFix_distractor:factor(condition)0.3                         2.578
## nFix_target:nFix_distractor:factor(condition)0.5                         3.716
## nFix_target:nFix_distractor:factor(condition)0.7                         1.105
## nFix_target:nFix_distractor:factor(condition)0.85                        3.144
## nFix_target:nFix_distractor:factor(condition)0.95                        1.421
## nFix_target:chosenItemdistractor:factor(condition)0.3                    3.286
## nFix_target:chosenItemdistractor:factor(condition)0.5                    2.748
## nFix_target:chosenItemdistractor:factor(condition)0.7                    1.873
## nFix_target:chosenItemdistractor:factor(condition)0.85                   2.446
## nFix_target:chosenItemdistractor:factor(condition)0.95                   0.012
## nFix_distractor:chosenItemdistractor:factor(condition)0.3               -0.860
## nFix_distractor:chosenItemdistractor:factor(condition)0.5               -0.375
## nFix_distractor:chosenItemdistractor:factor(condition)0.7               -1.589
## nFix_distractor:chosenItemdistractor:factor(condition)0.85               1.287
## nFix_distractor:chosenItemdistractor:factor(condition)0.95               0.183
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3   -1.597
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5   -0.900
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7    0.731
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85  -1.950
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95   0.235
##                                                                        Pr(>|t|)
## (Intercept)                                                            0.122146
## nFix_target                                                            1.37e-07
## nFix_distractor                                                        0.101654
## chosenItemdistractor                                                   0.000644
## factor(condition)0.3                                                   1.02e-06
## factor(condition)0.5                                                   1.63e-05
## factor(condition)0.7                                                   0.050329
## factor(condition)0.85                                                  0.201998
## factor(condition)0.95                                                  1.40e-06
## nFix_target:nFix_distractor                                            1.97e-12
## nFix_target:chosenItemdistractor                                       8.82e-08
## nFix_distractor:chosenItemdistractor                                   0.180631
## nFix_target:factor(condition)0.3                                       2.54e-05
## nFix_target:factor(condition)0.5                                       2.11e-05
## nFix_target:factor(condition)0.7                                       0.035715
## nFix_target:factor(condition)0.85                                      0.242528
## nFix_target:factor(condition)0.95                                      0.749759
## nFix_distractor:factor(condition)0.3                                   0.841020
## nFix_distractor:factor(condition)0.5                                   0.385347
## nFix_distractor:factor(condition)0.7                                   0.997957
## nFix_distractor:factor(condition)0.85                                  0.142973
## nFix_distractor:factor(condition)0.95                                  0.159709
## chosenItemdistractor:factor(condition)0.3                              0.680205
## chosenItemdistractor:factor(condition)0.5                              0.536032
## chosenItemdistractor:factor(condition)0.7                              0.360495
## chosenItemdistractor:factor(condition)0.85                             0.190214
## chosenItemdistractor:factor(condition)0.95                             0.307147
## nFix_target:nFix_distractor:chosenItemdistractor                       0.005240
## nFix_target:nFix_distractor:factor(condition)0.3                       0.009942
## nFix_target:nFix_distractor:factor(condition)0.5                       0.000203
## nFix_target:nFix_distractor:factor(condition)0.7                       0.269366
## nFix_target:nFix_distractor:factor(condition)0.85                      0.001667
## nFix_target:nFix_distractor:factor(condition)0.95                      0.155305
## nFix_target:chosenItemdistractor:factor(condition)0.3                  0.001017
## nFix_target:chosenItemdistractor:factor(condition)0.5                  0.005991
## nFix_target:chosenItemdistractor:factor(condition)0.7                  0.061029
## nFix_target:chosenItemdistractor:factor(condition)0.85                 0.014459
## nFix_target:chosenItemdistractor:factor(condition)0.95                 0.990679
## nFix_distractor:chosenItemdistractor:factor(condition)0.3              0.389820
## nFix_distractor:chosenItemdistractor:factor(condition)0.5              0.707683
## nFix_distractor:chosenItemdistractor:factor(condition)0.7              0.112137
## nFix_distractor:chosenItemdistractor:factor(condition)0.85             0.198231
## nFix_distractor:chosenItemdistractor:factor(condition)0.95             0.855069
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3  0.110227
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5  0.367968
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7  0.464988
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.051135
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.814275
##                                                                           
## (Intercept)                                                               
## nFix_target                                                            ***
## nFix_distractor                                                           
## chosenItemdistractor                                                   ***
## factor(condition)0.3                                                   ***
## factor(condition)0.5                                                   ***
## factor(condition)0.7                                                   .  
## factor(condition)0.85                                                     
## factor(condition)0.95                                                  ***
## nFix_target:nFix_distractor                                            ***
## nFix_target:chosenItemdistractor                                       ***
## nFix_distractor:chosenItemdistractor                                      
## nFix_target:factor(condition)0.3                                       ***
## nFix_target:factor(condition)0.5                                       ***
## nFix_target:factor(condition)0.7                                       *  
## nFix_target:factor(condition)0.85                                         
## nFix_target:factor(condition)0.95                                         
## nFix_distractor:factor(condition)0.3                                      
## nFix_distractor:factor(condition)0.5                                      
## nFix_distractor:factor(condition)0.7                                      
## nFix_distractor:factor(condition)0.85                                     
## nFix_distractor:factor(condition)0.95                                     
## chosenItemdistractor:factor(condition)0.3                                 
## chosenItemdistractor:factor(condition)0.5                                 
## chosenItemdistractor:factor(condition)0.7                                 
## chosenItemdistractor:factor(condition)0.85                                
## chosenItemdistractor:factor(condition)0.95                                
## nFix_target:nFix_distractor:chosenItemdistractor                       ** 
## nFix_target:nFix_distractor:factor(condition)0.3                       ** 
## nFix_target:nFix_distractor:factor(condition)0.5                       ***
## nFix_target:nFix_distractor:factor(condition)0.7                          
## nFix_target:nFix_distractor:factor(condition)0.85                      ** 
## nFix_target:nFix_distractor:factor(condition)0.95                         
## nFix_target:chosenItemdistractor:factor(condition)0.3                  ** 
## nFix_target:chosenItemdistractor:factor(condition)0.5                  ** 
## nFix_target:chosenItemdistractor:factor(condition)0.7                  .  
## nFix_target:chosenItemdistractor:factor(condition)0.85                 *  
## nFix_target:chosenItemdistractor:factor(condition)0.95                    
## nFix_distractor:chosenItemdistractor:factor(condition)0.3                 
## nFix_distractor:chosenItemdistractor:factor(condition)0.5                 
## nFix_distractor:chosenItemdistractor:factor(condition)0.7                 
## nFix_distractor:chosenItemdistractor:factor(condition)0.85                
## nFix_distractor:chosenItemdistractor:factor(condition)0.95                
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3     
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5     
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7     
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 .  
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9489 on 54594 degrees of freedom
## Multiple R-squared:  0.08988,    Adjusted R-squared:  0.08909 
## F-statistic: 114.7 on 47 and 54594 DF,  p-value: < 2.2e-16
Anova(f7)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                          Sum Sq    Df   F value
## nFix_target                                                  58     1   64.5764
## nFix_distractor                                             253     1  280.9254
## chosenItem                                                 2599     1 2886.3850
## factor(condition)                                          1399     5  310.6717
## nFix_target:nFix_distractor                                  77     1   85.6076
## nFix_target:chosenItem                                       42     1   46.2333
## nFix_distractor:chosenItem                                    0     1    0.0391
## nFix_target:factor(condition)                                59     5   13.0120
## nFix_distractor:factor(condition)                            28     5    6.1230
## chosenItem:factor(condition)                                 43     5    9.4889
## nFix_target:nFix_distractor:chosenItem                       19     1   21.5590
## nFix_target:nFix_distractor:factor(condition)                14     5    3.0520
## nFix_target:chosenItem:factor(condition)                     36     5    8.1054
## nFix_distractor:chosenItem:factor(condition)                 29     5    6.4248
## nFix_target:nFix_distractor:chosenItem:factor(condition)     10     5    2.1748
## Residuals                                                 49153 54594          
##                                                             Pr(>F)    
## nFix_target                                              9.471e-16 ***
## nFix_distractor                                          < 2.2e-16 ***
## chosenItem                                               < 2.2e-16 ***
## factor(condition)                                        < 2.2e-16 ***
## nFix_target:nFix_distractor                              < 2.2e-16 ***
## nFix_target:chosenItem                                   1.061e-11 ***
## nFix_distractor:chosenItem                                0.843216    
## nFix_target:factor(condition)                            1.108e-12 ***
## nFix_distractor:factor(condition)                        1.120e-05 ***
## chosenItem:factor(condition)                             4.654e-09 ***
## nFix_target:nFix_distractor:chosenItem                   3.439e-06 ***
## nFix_target:nFix_distractor:factor(condition)             0.009315 ** 
## nFix_target:chosenItem:factor(condition)                 1.177e-07 ***
## nFix_distractor:chosenItem:factor(condition)             5.637e-06 ***
## nFix_target:nFix_distractor:chosenItem:factor(condition)  0.053948 .  
## Residuals                                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f7, terms = c("nFix_target", "nFix_distractor", "chosenItem", "condition")))

f8 <- lm(conf_normalized ~ nFix_target * factor(nFix_distractor) * chosenItem * factor(condition), 
         data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & 
                           dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f8)
## 
## Call:
## lm(formula = conf_normalized ~ nFix_target * factor(nFix_distractor) * 
##     chosenItem * factor(condition), data = subset(dat, dat$nFix_target <= 
##     3 & dat$nFix_distractor <= 3 & dat$chosenItem != "dud" & 
##     dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1037 -0.6837  0.0979  0.7374  3.3339 
## 
## Coefficients:
##                                                                                  Estimate
## (Intercept)                                                                      0.013880
## nFix_target                                                                      0.224227
## factor(nFix_distractor)1                                                         0.156638
## factor(nFix_distractor)2                                                         0.086153
## factor(nFix_distractor)3                                                         0.388551
## chosenItemdistractor                                                            -0.114684
## factor(condition)0.3                                                             0.379474
## factor(condition)0.5                                                             0.330954
## factor(condition)0.7                                                             0.291485
## factor(condition)0.85                                                           -0.008700
## factor(condition)0.95                                                           -0.231756
## nFix_target:factor(nFix_distractor)1                                            -0.227801
## nFix_target:factor(nFix_distractor)2                                            -0.345138
## nFix_target:factor(nFix_distractor)3                                            -0.632537
## nFix_target:chosenItemdistractor                                                -0.653058
## factor(nFix_distractor)1:chosenItemdistractor                                   -0.310632
## factor(nFix_distractor)2:chosenItemdistractor                                   -0.574102
## factor(nFix_distractor)3:chosenItemdistractor                                   -0.230412
## nFix_target:factor(condition)0.3                                                -0.219729
## nFix_target:factor(condition)0.5                                                -0.222327
## nFix_target:factor(condition)0.7                                                -0.255858
## nFix_target:factor(condition)0.85                                               -0.096152
## nFix_target:factor(condition)0.95                                               -0.030914
## factor(nFix_distractor)1:factor(condition)0.3                                   -0.133505
## factor(nFix_distractor)2:factor(condition)0.3                                    0.241369
## factor(nFix_distractor)3:factor(condition)0.3                                   -0.814479
## factor(nFix_distractor)1:factor(condition)0.5                                   -0.120937
## factor(nFix_distractor)2:factor(condition)0.5                                   -0.167436
## factor(nFix_distractor)3:factor(condition)0.5                                    0.060577
## factor(nFix_distractor)1:factor(condition)0.7                                   -0.270748
## factor(nFix_distractor)2:factor(condition)0.7                                    0.166134
## factor(nFix_distractor)3:factor(condition)0.7                                   -0.704050
## factor(nFix_distractor)1:factor(condition)0.85                                  -0.181347
## factor(nFix_distractor)2:factor(condition)0.85                                  -0.183849
## factor(nFix_distractor)3:factor(condition)0.85                                   0.198280
## factor(nFix_distractor)1:factor(condition)0.95                                  -0.176575
## factor(nFix_distractor)2:factor(condition)0.95                                  -0.085037
## factor(nFix_distractor)3:factor(condition)0.95                                  -0.558574
## chosenItemdistractor:factor(condition)0.3                                       -0.086200
## chosenItemdistractor:factor(condition)0.5                                       -0.365645
## chosenItemdistractor:factor(condition)0.7                                       -0.193289
## chosenItemdistractor:factor(condition)0.85                                      -0.553865
## chosenItemdistractor:factor(condition)0.95                                      -0.027863
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor                        0.406096
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor                        0.728874
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor                        0.473406
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3                        0.146061
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3                       -0.057417
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3                        0.922019
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5                        0.154381
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5                        0.262101
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5                        0.340447
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7                        0.263120
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7                        0.069194
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7                        0.432169
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85                       0.182931
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85                       0.148118
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85                       0.289837
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95                       0.122103
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95                       0.073883
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95                       0.354767
## nFix_target:chosenItemdistractor:factor(condition)0.3                            0.461829
## nFix_target:chosenItemdistractor:factor(condition)0.5                            0.565701
## nFix_target:chosenItemdistractor:factor(condition)0.7                            0.468366
## nFix_target:chosenItemdistractor:factor(condition)0.85                           0.704065
## nFix_target:chosenItemdistractor:factor(condition)0.95                           0.215548
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3               0.090408
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3              -0.003854
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3              -0.134253
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5               0.303967
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5               0.453952
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5              -0.376023
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7               0.241931
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7              -0.009917
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7              -0.033059
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85              0.676994
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85              0.662522
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85             -0.055161
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95              0.194953
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95              0.267001
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95              0.375746
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3  -0.265984
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3  -0.344746
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3  -0.745450
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5  -0.365642
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5  -0.657525
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5  -0.114782
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7  -0.273971
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7  -0.238246
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7  -0.116207
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 -0.721380
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 -0.742785
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 -0.433151
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 -0.233827
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 -0.219209
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 -0.363323
##                                                                                 Std. Error
## (Intercept)                                                                       0.071069
## nFix_target                                                                       0.061351
## factor(nFix_distractor)1                                                          0.077590
## factor(nFix_distractor)2                                                          0.091027
## factor(nFix_distractor)3                                                          0.158264
## chosenItemdistractor                                                              0.139875
## factor(condition)0.3                                                              0.093947
## factor(condition)0.5                                                              0.091100
## factor(condition)0.7                                                              0.085821
## factor(condition)0.85                                                             0.083946
## factor(condition)0.95                                                             0.084904
## nFix_target:factor(nFix_distractor)1                                              0.065277
## nFix_target:factor(nFix_distractor)2                                              0.070897
## nFix_target:factor(nFix_distractor)3                                              0.102498
## nFix_target:chosenItemdistractor                                                  0.152040
## factor(nFix_distractor)1:chosenItemdistractor                                     0.151476
## factor(nFix_distractor)2:chosenItemdistractor                                     0.185971
## factor(nFix_distractor)3:chosenItemdistractor                                     0.274720
## nFix_target:factor(condition)0.3                                                  0.083056
## nFix_target:factor(condition)0.5                                                  0.080258
## nFix_target:factor(condition)0.7                                                  0.075960
## nFix_target:factor(condition)0.85                                                 0.073448
## nFix_target:factor(condition)0.95                                                 0.074392
## factor(nFix_distractor)1:factor(condition)0.3                                     0.103383
## factor(nFix_distractor)2:factor(condition)0.3                                     0.126437
## factor(nFix_distractor)3:factor(condition)0.3                                     0.220953
## factor(nFix_distractor)1:factor(condition)0.5                                     0.100848
## factor(nFix_distractor)2:factor(condition)0.5                                     0.123977
## factor(nFix_distractor)3:factor(condition)0.5                                     0.212273
## factor(nFix_distractor)1:factor(condition)0.7                                     0.095759
## factor(nFix_distractor)2:factor(condition)0.7                                     0.121199
## factor(nFix_distractor)3:factor(condition)0.7                                     0.272520
## factor(nFix_distractor)1:factor(condition)0.85                                    0.094355
## factor(nFix_distractor)2:factor(condition)0.85                                    0.121311
## factor(nFix_distractor)3:factor(condition)0.85                                    0.224550
## factor(nFix_distractor)1:factor(condition)0.95                                    0.095090
## factor(nFix_distractor)2:factor(condition)0.95                                    0.121398
## factor(nFix_distractor)3:factor(condition)0.95                                    0.239291
## chosenItemdistractor:factor(condition)0.3                                         0.179339
## chosenItemdistractor:factor(condition)0.5                                         0.176609
## chosenItemdistractor:factor(condition)0.7                                         0.170552
## chosenItemdistractor:factor(condition)0.85                                        0.165992
## chosenItemdistractor:factor(condition)0.95                                        0.175102
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor                         0.158930
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor                         0.175707
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor                         0.205425
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3                         0.088803
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3                         0.099673
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3                         0.152128
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5                         0.086213
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5                         0.096489
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5                         0.135326
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7                         0.082048
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7                         0.092864
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7                         0.191686
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85                        0.080024
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85                        0.092842
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85                        0.134419
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95                        0.080690
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95                        0.092376
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95                        0.155625
## nFix_target:chosenItemdistractor:factor(condition)0.3                             0.186571
## nFix_target:chosenItemdistractor:factor(condition)0.5                             0.187549
## nFix_target:chosenItemdistractor:factor(condition)0.7                             0.185461
## nFix_target:chosenItemdistractor:factor(condition)0.85                            0.179388
## nFix_target:chosenItemdistractor:factor(condition)0.95                            0.187518
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3                0.196624
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3                0.247310
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3                0.424169
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5                0.193977
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5                0.245278
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5                0.408879
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7                0.187542
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7                0.240477
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7                0.470384
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85               0.184152
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85               0.235972
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85               0.475908
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95               0.192138
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95               0.244049
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95               0.430445
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3    0.197171
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3    0.222538
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3    0.287262
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5    0.198562
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5    0.220738
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5    0.273356
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7    0.196442
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7    0.219450
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7    0.368015
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85   0.191337
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85   0.214888
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85   0.305750
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95   0.198280
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95   0.221801
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95   0.299339
##                                                                                 t value
## (Intercept)                                                                       0.195
## nFix_target                                                                       3.655
## factor(nFix_distractor)1                                                          2.019
## factor(nFix_distractor)2                                                          0.946
## factor(nFix_distractor)3                                                          2.455
## chosenItemdistractor                                                             -0.820
## factor(condition)0.3                                                              4.039
## factor(condition)0.5                                                              3.633
## factor(condition)0.7                                                              3.396
## factor(condition)0.85                                                            -0.104
## factor(condition)0.95                                                            -2.730
## nFix_target:factor(nFix_distractor)1                                             -3.490
## nFix_target:factor(nFix_distractor)2                                             -4.868
## nFix_target:factor(nFix_distractor)3                                             -6.171
## nFix_target:chosenItemdistractor                                                 -4.295
## factor(nFix_distractor)1:chosenItemdistractor                                    -2.051
## factor(nFix_distractor)2:chosenItemdistractor                                    -3.087
## factor(nFix_distractor)3:chosenItemdistractor                                    -0.839
## nFix_target:factor(condition)0.3                                                 -2.646
## nFix_target:factor(condition)0.5                                                 -2.770
## nFix_target:factor(condition)0.7                                                 -3.368
## nFix_target:factor(condition)0.85                                                -1.309
## nFix_target:factor(condition)0.95                                                -0.416
## factor(nFix_distractor)1:factor(condition)0.3                                    -1.291
## factor(nFix_distractor)2:factor(condition)0.3                                     1.909
## factor(nFix_distractor)3:factor(condition)0.3                                    -3.686
## factor(nFix_distractor)1:factor(condition)0.5                                    -1.199
## factor(nFix_distractor)2:factor(condition)0.5                                    -1.351
## factor(nFix_distractor)3:factor(condition)0.5                                     0.285
## factor(nFix_distractor)1:factor(condition)0.7                                    -2.827
## factor(nFix_distractor)2:factor(condition)0.7                                     1.371
## factor(nFix_distractor)3:factor(condition)0.7                                    -2.583
## factor(nFix_distractor)1:factor(condition)0.85                                   -1.922
## factor(nFix_distractor)2:factor(condition)0.85                                   -1.516
## factor(nFix_distractor)3:factor(condition)0.85                                    0.883
## factor(nFix_distractor)1:factor(condition)0.95                                   -1.857
## factor(nFix_distractor)2:factor(condition)0.95                                   -0.700
## factor(nFix_distractor)3:factor(condition)0.95                                   -2.334
## chosenItemdistractor:factor(condition)0.3                                        -0.481
## chosenItemdistractor:factor(condition)0.5                                        -2.070
## chosenItemdistractor:factor(condition)0.7                                        -1.133
## chosenItemdistractor:factor(condition)0.85                                       -3.337
## chosenItemdistractor:factor(condition)0.95                                       -0.159
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor                         2.555
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor                         4.148
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor                         2.305
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3                         1.645
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3                        -0.576
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3                         6.061
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5                         1.791
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5                         2.716
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5                         2.516
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7                         3.207
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7                         0.745
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7                         2.255
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85                        2.286
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85                        1.595
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85                        2.156
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95                        1.513
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95                        0.800
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95                        2.280
## nFix_target:chosenItemdistractor:factor(condition)0.3                             2.475
## nFix_target:chosenItemdistractor:factor(condition)0.5                             3.016
## nFix_target:chosenItemdistractor:factor(condition)0.7                             2.525
## nFix_target:chosenItemdistractor:factor(condition)0.85                            3.925
## nFix_target:chosenItemdistractor:factor(condition)0.95                            1.149
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3                0.460
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3               -0.016
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3               -0.317
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5                1.567
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5                1.851
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5               -0.920
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7                1.290
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7               -0.041
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7               -0.070
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85               3.676
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85               2.808
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85              -0.116
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95               1.015
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95               1.094
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95               0.873
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3   -1.349
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3   -1.549
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3   -2.595
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5   -1.841
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5   -2.979
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5   -0.420
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7   -1.395
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7   -1.086
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7   -0.316
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85  -3.770
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85  -3.457
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85  -1.417
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95  -1.179
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95  -0.988
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95  -1.214
##                                                                                 Pr(>|t|)
## (Intercept)                                                                     0.845154
## nFix_target                                                                     0.000258
## factor(nFix_distractor)1                                                        0.043514
## factor(nFix_distractor)2                                                        0.343921
## factor(nFix_distractor)3                                                        0.014089
## chosenItemdistractor                                                            0.412276
## factor(condition)0.3                                                            5.37e-05
## factor(condition)0.5                                                            0.000281
## factor(condition)0.7                                                            0.000683
## factor(condition)0.85                                                           0.917455
## factor(condition)0.95                                                           0.006343
## nFix_target:factor(nFix_distractor)1                                            0.000484
## nFix_target:factor(nFix_distractor)2                                            1.13e-06
## nFix_target:factor(nFix_distractor)3                                            6.82e-10
## nFix_target:chosenItemdistractor                                                1.75e-05
## factor(nFix_distractor)1:chosenItemdistractor                                   0.040301
## factor(nFix_distractor)2:chosenItemdistractor                                   0.002023
## factor(nFix_distractor)3:chosenItemdistractor                                   0.401631
## nFix_target:factor(condition)0.3                                                0.008158
## nFix_target:factor(condition)0.5                                                0.005605
## nFix_target:factor(condition)0.7                                                0.000757
## nFix_target:factor(condition)0.85                                               0.190498
## nFix_target:factor(condition)0.95                                               0.677740
## factor(nFix_distractor)1:factor(condition)0.3                                   0.196582
## factor(nFix_distractor)2:factor(condition)0.3                                   0.056267
## factor(nFix_distractor)3:factor(condition)0.3                                   0.000228
## factor(nFix_distractor)1:factor(condition)0.5                                   0.230453
## factor(nFix_distractor)2:factor(condition)0.5                                   0.176848
## factor(nFix_distractor)3:factor(condition)0.5                                   0.775361
## factor(nFix_distractor)1:factor(condition)0.7                                   0.004695
## factor(nFix_distractor)2:factor(condition)0.7                                   0.170456
## factor(nFix_distractor)3:factor(condition)0.7                                   0.009784
## factor(nFix_distractor)1:factor(condition)0.85                                  0.054614
## factor(nFix_distractor)2:factor(condition)0.85                                  0.129648
## factor(nFix_distractor)3:factor(condition)0.85                                  0.377234
## factor(nFix_distractor)1:factor(condition)0.95                                  0.063327
## factor(nFix_distractor)2:factor(condition)0.95                                  0.483629
## factor(nFix_distractor)3:factor(condition)0.95                                  0.019584
## chosenItemdistractor:factor(condition)0.3                                       0.630765
## chosenItemdistractor:factor(condition)0.5                                       0.038423
## chosenItemdistractor:factor(condition)0.7                                       0.257087
## chosenItemdistractor:factor(condition)0.85                                      0.000848
## chosenItemdistractor:factor(condition)0.95                                      0.873572
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor                       0.010616
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor                       3.36e-05
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor                       0.021197
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3                       0.100021
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3                       0.564579
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3                       1.36e-09
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5                       0.073346
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5                       0.006602
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5                       0.011880
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7                       0.001342
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7                       0.456207
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7                       0.024164
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85                      0.022261
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85                      0.110633
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85                      0.031071
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95                      0.130225
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95                      0.423827
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95                      0.022634
## nFix_target:chosenItemdistractor:factor(condition)0.3                           0.013313
## nFix_target:chosenItemdistractor:factor(condition)0.5                           0.002560
## nFix_target:chosenItemdistractor:factor(condition)0.7                           0.011559
## nFix_target:chosenItemdistractor:factor(condition)0.85                          8.69e-05
## nFix_target:chosenItemdistractor:factor(condition)0.95                          0.250363
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3              0.645660
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3              0.987568
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3              0.751618
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5              0.117115
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5              0.064209
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5              0.357763
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7              0.197051
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7              0.967104
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7              0.943970
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85             0.000237
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85             0.004993
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85             0.907726
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95             0.310276
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95             0.273939
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95             0.382708
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3  0.177341
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3  0.121350
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3  0.009461
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5  0.065561
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5  0.002895
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5  0.674559
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7  0.163121
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7  0.277639
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7  0.752181
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.000163
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.000547
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 0.156581
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.238295
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.323002
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.224849
##                                                                                    
## (Intercept)                                                                        
## nFix_target                                                                     ***
## factor(nFix_distractor)1                                                        *  
## factor(nFix_distractor)2                                                           
## factor(nFix_distractor)3                                                        *  
## chosenItemdistractor                                                               
## factor(condition)0.3                                                            ***
## factor(condition)0.5                                                            ***
## factor(condition)0.7                                                            ***
## factor(condition)0.85                                                              
## factor(condition)0.95                                                           ** 
## nFix_target:factor(nFix_distractor)1                                            ***
## nFix_target:factor(nFix_distractor)2                                            ***
## nFix_target:factor(nFix_distractor)3                                            ***
## nFix_target:chosenItemdistractor                                                ***
## factor(nFix_distractor)1:chosenItemdistractor                                   *  
## factor(nFix_distractor)2:chosenItemdistractor                                   ** 
## factor(nFix_distractor)3:chosenItemdistractor                                      
## nFix_target:factor(condition)0.3                                                ** 
## nFix_target:factor(condition)0.5                                                ** 
## nFix_target:factor(condition)0.7                                                ***
## nFix_target:factor(condition)0.85                                                  
## nFix_target:factor(condition)0.95                                                  
## factor(nFix_distractor)1:factor(condition)0.3                                      
## factor(nFix_distractor)2:factor(condition)0.3                                   .  
## factor(nFix_distractor)3:factor(condition)0.3                                   ***
## factor(nFix_distractor)1:factor(condition)0.5                                      
## factor(nFix_distractor)2:factor(condition)0.5                                      
## factor(nFix_distractor)3:factor(condition)0.5                                      
## factor(nFix_distractor)1:factor(condition)0.7                                   ** 
## factor(nFix_distractor)2:factor(condition)0.7                                      
## factor(nFix_distractor)3:factor(condition)0.7                                   ** 
## factor(nFix_distractor)1:factor(condition)0.85                                  .  
## factor(nFix_distractor)2:factor(condition)0.85                                     
## factor(nFix_distractor)3:factor(condition)0.85                                     
## factor(nFix_distractor)1:factor(condition)0.95                                  .  
## factor(nFix_distractor)2:factor(condition)0.95                                     
## factor(nFix_distractor)3:factor(condition)0.95                                  *  
## chosenItemdistractor:factor(condition)0.3                                          
## chosenItemdistractor:factor(condition)0.5                                       *  
## chosenItemdistractor:factor(condition)0.7                                          
## chosenItemdistractor:factor(condition)0.85                                      ***
## chosenItemdistractor:factor(condition)0.95                                         
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor                       *  
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor                       ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor                       *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3                          
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3                          
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3                       ***
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5                       .  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5                       ** 
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5                       *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7                       ** 
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7                          
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7                       *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85                      *  
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85                         
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85                      *  
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95                         
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95                         
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95                      *  
## nFix_target:chosenItemdistractor:factor(condition)0.3                           *  
## nFix_target:chosenItemdistractor:factor(condition)0.5                           ** 
## nFix_target:chosenItemdistractor:factor(condition)0.7                           *  
## nFix_target:chosenItemdistractor:factor(condition)0.85                          ***
## nFix_target:chosenItemdistractor:factor(condition)0.95                             
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3                 
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3                 
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3                 
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5                 
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5              .  
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5                 
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7                 
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7                 
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7                 
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85             ***
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85             ** 
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85                
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95                
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95                
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95                
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3     
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3     
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3  ** 
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5  .  
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5  ** 
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5     
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7     
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7     
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7     
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 ***
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85    
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95    
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95    
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9468 on 54546 degrees of freedom
## Multiple R-squared:  0.09465,    Adjusted R-squared:  0.09308 
## F-statistic: 60.03 on 95 and 54546 DF,  p-value: < 2.2e-16
Anova(f8)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                                  Sum Sq    Df
## nFix_target                                                          60     1
## factor(nFix_distractor)                                             280     3
## chosenItem                                                         2559     1
## factor(condition)                                                  1400     5
## nFix_target:factor(nFix_distractor)                                  84     3
## nFix_target:chosenItem                                               40     1
## factor(nFix_distractor):chosenItem                                    4     3
## nFix_target:factor(condition)                                        53     5
## factor(nFix_distractor):factor(condition)                           115    15
## chosenItem:factor(condition)                                         40     5
## nFix_target:factor(nFix_distractor):chosenItem                       48     3
## nFix_target:factor(nFix_distractor):factor(condition)                74    15
## nFix_target:chosenItem:factor(condition)                             33     5
## factor(nFix_distractor):chosenItem:factor(condition)                 60    15
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition)     39    15
## Residuals                                                         48895 54546
##                                                                    F value
## nFix_target                                                        67.3104
## factor(nFix_distractor)                                           104.2530
## chosenItem                                                       2854.8848
## factor(condition)                                                 312.3903
## nFix_target:factor(nFix_distractor)                                31.1949
## nFix_target:chosenItem                                             44.9883
## factor(nFix_distractor):chosenItem                                  1.5052
## nFix_target:factor(condition)                                      11.8591
## factor(nFix_distractor):factor(condition)                           8.5224
## chosenItem:factor(condition)                                        8.9407
## nFix_target:factor(nFix_distractor):chosenItem                     17.9054
## nFix_target:factor(nFix_distractor):factor(condition)               5.4674
## nFix_target:chosenItem:factor(condition)                            7.2520
## factor(nFix_distractor):chosenItem:factor(condition)                4.4514
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition)    2.8669
## Residuals                                                                 
##                                                                     Pr(>F)    
## nFix_target                                                      2.370e-16 ***
## factor(nFix_distractor)                                          < 2.2e-16 ***
## chosenItem                                                       < 2.2e-16 ***
## factor(condition)                                                < 2.2e-16 ***
## nFix_target:factor(nFix_distractor)                              < 2.2e-16 ***
## nFix_target:chosenItem                                           2.001e-11 ***
## factor(nFix_distractor):chosenItem                               0.2109129    
## nFix_target:factor(condition)                                    1.724e-11 ***
## factor(nFix_distractor):factor(condition)                        < 2.2e-16 ***
## chosenItem:factor(condition)                                     1.680e-08 ***
## nFix_target:factor(nFix_distractor):chosenItem                   1.306e-11 ***
## nFix_target:factor(nFix_distractor):factor(condition)            3.056e-11 ***
## nFix_target:chosenItem:factor(condition)                         8.469e-07 ***
## factor(nFix_distractor):chosenItem:factor(condition)             1.691e-08 ***
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition) 0.0001579 ***
## Residuals                                                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f8, terms = c("nFix_target", "nFix_distractor", "chosenItem", "condition")))

dur_target, dur_distractorの両者で反応正誤を説明

hist(dat$dur_target)

hist(dat$dur_distractor)

plot(dat$dur_target, dat$dur_distractor)

cor(dat$dur_target, dat$dur_distractor)
## [1] 0.4845666
# condition aggregated
ggplot(dat, aes(x = dur_target, y = corr, color = factor(q_dur_distractor))) + 
    geom_count(alpha = 0.5) + stat_smooth() +
    scale_x_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1))
## Warning: Removed 61 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 61 rows containing non-finite values (`stat_smooth()`).

f9 <- glm(corr ~ dur_target * dur_distractor, family = binomial, data = dat)
summary(f9)
## 
## Call:
## glm(formula = corr ~ dur_target * dur_distractor, family = binomial, 
##     data = dat)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7958   0.4568   0.6509   0.7500   1.3507  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.82597    0.02468  33.473  < 2e-16 ***
## dur_target                 3.71125    0.10864  34.160  < 2e-16 ***
## dur_distractor            -0.81541    0.10136  -8.045 8.63e-16 ***
## dur_target:dur_distractor -3.98799    0.28047 -14.219  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 66331  on 62108  degrees of freedom
## Residual deviance: 64494  on 62105  degrees of freedom
## AIC: 64502
## 
## Number of Fisher Scoring iterations: 4
Anova(f9)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                           LR Chisq Df Pr(>Chisq)    
## dur_target                 1450.77  1  < 2.2e-16 ***
## dur_distractor              941.83  1  < 2.2e-16 ***
## dur_target:dur_distractor   195.03  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f9, terms = c("dur_target", "dur_distractor")))
## Data were 'prettified'. Consider using `terms="dur_target [all]"` to get
##   smooth plots.

f10 <- glm(corr ~ dur_target * factor(q_dur_distractor), data = dat)
summary(f10)
## 
## Call:
## glm(formula = corr ~ dur_target * factor(q_dur_distractor), data = dat)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.10543   0.07347   0.18839   0.25983   0.36223  
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.762431   0.005312 143.518  < 2e-16
## dur_target                               0.388316   0.021598  17.979  < 2e-16
## factor(q_dur_distractor)0.25            -0.026849   0.008012  -3.351 0.000806
## factor(q_dur_distractor)0.5             -0.115438   0.007831 -14.741  < 2e-16
## factor(q_dur_distractor)0.75            -0.124663   0.008041 -15.504  < 2e-16
## dur_target:factor(q_dur_distractor)0.25 -0.090884   0.029929  -3.037 0.002393
## dur_target:factor(q_dur_distractor)0.5  -0.015596   0.028614  -0.545 0.585728
## dur_target:factor(q_dur_distractor)0.75 -0.146424   0.027646  -5.296 1.19e-07
##                                            
## (Intercept)                             ***
## dur_target                              ***
## factor(q_dur_distractor)0.25            ***
## factor(q_dur_distractor)0.5             ***
## factor(q_dur_distractor)0.75            ***
## dur_target:factor(q_dur_distractor)0.25 ** 
## dur_target:factor(q_dur_distractor)0.5     
## dur_target:factor(q_dur_distractor)0.75 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1689875)
## 
##     Null deviance: 10853  on 62108  degrees of freedom
## Residual deviance: 10494  on 62101  degrees of freedom
## AIC: 65842
## 
## Number of Fisher Scoring iterations: 2
Anova(f10)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                     LR Chisq Df Pr(>Chisq)    
## dur_target                           1080.61  1  < 2.2e-16 ***
## factor(q_dur_distractor)             1397.03  3  < 2.2e-16 ***
## dur_target:factor(q_dur_distractor)    39.53  3  1.339e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f10, terms = c("dur_target", "q_dur_distractor")))

# condition separated
ggplot(dat, aes(x = dur_target, y = corr, color = factor(q_dur_distractor))) + 
    geom_count(alpha = 0.5) + stat_smooth() +
    scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + facet_wrap(. ~ condition)
## Warning: Removed 2611 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2611 rows containing non-finite values (`stat_smooth()`).

f11 <- glm(corr ~ dur_target * dur_distractor * factor(condition), family = binomial, data = dat)
summary(f11)
## 
## Call:
## glm(formula = corr ~ dur_target * dur_distractor * factor(condition), 
##     family = binomial, data = dat)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7880   0.4227   0.6510   0.7414   1.4485  
## 
## Coefficients:
##                                                 Estimate Std. Error z value
## (Intercept)                                      0.84176    0.06771  12.433
## dur_target                                       2.65817    0.25995  10.226
## dur_distractor                                   0.01684    0.24333   0.069
## factor(condition)0.3                             0.01274    0.09133   0.139
## factor(condition)0.5                             0.03939    0.09258   0.425
## factor(condition)0.7                             0.16433    0.09315   1.764
## factor(condition)0.85                            0.01185    0.08946   0.132
## factor(condition)0.95                           -0.23963    0.08772  -2.732
## dur_target:dur_distractor                       -3.54188    0.61348  -5.773
## dur_target:factor(condition)0.3                 -0.13800    0.36424  -0.379
## dur_target:factor(condition)0.5                  0.95942    0.38310   2.504
## dur_target:factor(condition)0.7                  2.02282    0.40638   4.978
## dur_target:factor(condition)0.85                 2.08693    0.37794   5.522
## dur_target:factor(condition)0.95                 1.69559    0.36644   4.627
## dur_distractor:factor(condition)0.3              0.53667    0.34039   1.577
## dur_distractor:factor(condition)0.5             -0.54852    0.35672  -1.538
## dur_distractor:factor(condition)0.7             -2.19732    0.37488  -5.861
## dur_distractor:factor(condition)0.85            -1.69818    0.35276  -4.814
## dur_distractor:factor(condition)0.95            -1.58404    0.34817  -4.550
## dur_target:dur_distractor:factor(condition)0.3  -1.04099    0.85955  -1.211
## dur_target:dur_distractor:factor(condition)0.5  -0.71756    0.94609  -0.758
## dur_target:dur_distractor:factor(condition)0.7   0.46608    1.07119   0.435
## dur_target:dur_distractor:factor(condition)0.85 -1.26941    0.98389  -1.290
## dur_target:dur_distractor:factor(condition)0.95 -0.51354    0.99204  -0.518
##                                                 Pr(>|z|)    
## (Intercept)                                      < 2e-16 ***
## dur_target                                       < 2e-16 ***
## dur_distractor                                    0.9448    
## factor(condition)0.3                              0.8891    
## factor(condition)0.5                              0.6705    
## factor(condition)0.7                              0.0777 .  
## factor(condition)0.85                             0.8946    
## factor(condition)0.95                             0.0063 ** 
## dur_target:dur_distractor                       7.77e-09 ***
## dur_target:factor(condition)0.3                   0.7048    
## dur_target:factor(condition)0.5                   0.0123 *  
## dur_target:factor(condition)0.7                 6.44e-07 ***
## dur_target:factor(condition)0.85                3.36e-08 ***
## dur_target:factor(condition)0.95                3.71e-06 ***
## dur_distractor:factor(condition)0.3               0.1149    
## dur_distractor:factor(condition)0.5               0.1241    
## dur_distractor:factor(condition)0.7             4.59e-09 ***
## dur_distractor:factor(condition)0.85            1.48e-06 ***
## dur_distractor:factor(condition)0.95            5.38e-06 ***
## dur_target:dur_distractor:factor(condition)0.3    0.2259    
## dur_target:dur_distractor:factor(condition)0.5    0.4482    
## dur_target:dur_distractor:factor(condition)0.7    0.6635    
## dur_target:dur_distractor:factor(condition)0.85   0.1970    
## dur_target:dur_distractor:factor(condition)0.95   0.6047    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 66331  on 62108  degrees of freedom
## Residual deviance: 64064  on 62085  degrees of freedom
## AIC: 64112
## 
## Number of Fisher Scoring iterations: 4
Anova(f11)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                             LR Chisq Df Pr(>Chisq)    
## dur_target                                   1438.75  1     <2e-16 ***
## dur_distractor                                940.85  1     <2e-16 ***
## factor(condition)                             163.00  5     <2e-16 ***
## dur_target:dur_distractor                     195.48  1     <2e-16 ***
## dur_target:factor(condition)                  174.43  5     <2e-16 ***
## dur_distractor:factor(condition)              218.19  5     <2e-16 ***
## dur_target:dur_distractor:factor(condition)     3.75  5     0.5854    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f11, terms = c("dur_target", "dur_distractor", "condition")))
## Data were 'prettified'. Consider using `terms="dur_target [all]"` to get
##   smooth plots.

f12 <- glm(corr ~ dur_target * factor(q_dur_distractor) * factor(condition), data = dat)
summary(f12)
## 
## Call:
## glm(formula = corr ~ dur_target * factor(q_dur_distractor) * 
##     factor(condition), data = dat)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1449   0.0653   0.1871   0.2560   0.4893  
## 
## Coefficients:
##                                                                Estimate
## (Intercept)                                                    0.767981
## dur_target                                                     0.290692
## factor(q_dur_distractor)0.25                                  -0.017594
## factor(q_dur_distractor)0.5                                   -0.080322
## factor(q_dur_distractor)0.75                                  -0.056066
## factor(condition)0.3                                           0.016987
## factor(condition)0.5                                           0.007693
## factor(condition)0.7                                           0.038352
## factor(condition)0.85                                         -0.003807
## factor(condition)0.95                                         -0.067367
## dur_target:factor(q_dur_distractor)0.25                       -0.118833
## dur_target:factor(q_dur_distractor)0.5                        -0.016212
## dur_target:factor(q_dur_distractor)0.75                       -0.189773
## dur_target:factor(condition)0.3                               -0.068680
## dur_target:factor(condition)0.5                                0.111667
## dur_target:factor(condition)0.7                                0.081390
## dur_target:factor(condition)0.85                               0.140284
## dur_target:factor(condition)0.95                               0.202478
## factor(q_dur_distractor)0.25:factor(condition)0.3             -0.013091
## factor(q_dur_distractor)0.5:factor(condition)0.3              -0.032889
## factor(q_dur_distractor)0.75:factor(condition)0.3              0.003923
## factor(q_dur_distractor)0.25:factor(condition)0.5              0.021556
## factor(q_dur_distractor)0.5:factor(condition)0.5              -0.017999
## factor(q_dur_distractor)0.75:factor(condition)0.5             -0.078283
## factor(q_dur_distractor)0.25:factor(condition)0.7             -0.042500
## factor(q_dur_distractor)0.5:factor(condition)0.7              -0.084823
## factor(q_dur_distractor)0.75:factor(condition)0.7             -0.181827
## factor(q_dur_distractor)0.25:factor(condition)0.85            -0.017631
## factor(q_dur_distractor)0.5:factor(condition)0.85             -0.088208
## factor(q_dur_distractor)0.75:factor(condition)0.85            -0.089538
## factor(q_dur_distractor)0.25:factor(condition)0.95            -0.033068
## factor(q_dur_distractor)0.5:factor(condition)0.95             -0.015152
## factor(q_dur_distractor)0.75:factor(condition)0.95            -0.133848
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3   0.086707
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3    0.170993
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3  -0.033405
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5  -0.104934
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5   -0.128850
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5   0.121468
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7   0.141663
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7    0.123292
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7   0.238216
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85  0.070444
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85   0.101571
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 -0.004519
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95  0.126462
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95  -0.136963
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95  0.136790
##                                                               Std. Error
## (Intercept)                                                     0.017081
## dur_target                                                      0.069478
## factor(q_dur_distractor)0.25                                    0.023364
## factor(q_dur_distractor)0.5                                     0.021810
## factor(q_dur_distractor)0.75                                    0.021786
## factor(condition)0.3                                            0.022428
## factor(condition)0.5                                            0.021763
## factor(condition)0.7                                            0.021192
## factor(condition)0.85                                           0.020645
## factor(condition)0.95                                           0.020387
## dur_target:factor(q_dur_distractor)0.25                         0.085597
## dur_target:factor(q_dur_distractor)0.5                          0.080255
## dur_target:factor(q_dur_distractor)0.75                         0.078348
## dur_target:factor(condition)0.3                                 0.094088
## dur_target:factor(condition)0.5                                 0.089734
## dur_target:factor(condition)0.7                                 0.087712
## dur_target:factor(condition)0.85                                0.082671
## dur_target:factor(condition)0.95                                0.081802
## factor(q_dur_distractor)0.25:factor(condition)0.3               0.031199
## factor(q_dur_distractor)0.5:factor(condition)0.3                0.030108
## factor(q_dur_distractor)0.75:factor(condition)0.3               0.029657
## factor(q_dur_distractor)0.25:factor(condition)0.5               0.030764
## factor(q_dur_distractor)0.5:factor(condition)0.5                0.029148
## factor(q_dur_distractor)0.75:factor(condition)0.5               0.029539
## factor(q_dur_distractor)0.25:factor(condition)0.7               0.030067
## factor(q_dur_distractor)0.5:factor(condition)0.7                0.028627
## factor(q_dur_distractor)0.75:factor(condition)0.7               0.029661
## factor(q_dur_distractor)0.25:factor(condition)0.85              0.029778
## factor(q_dur_distractor)0.5:factor(condition)0.85               0.028780
## factor(q_dur_distractor)0.75:factor(condition)0.85              0.029344
## factor(q_dur_distractor)0.25:factor(condition)0.95              0.029547
## factor(q_dur_distractor)0.5:factor(condition)0.95               0.028238
## factor(q_dur_distractor)0.75:factor(condition)0.95              0.028893
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3    0.117564
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3     0.113663
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3    0.107724
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5    0.115212
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5     0.107537
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5    0.105484
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7    0.113424
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7     0.106263
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7    0.105998
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85   0.109084
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85    0.105630
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85   0.102661
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95   0.108222
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95    0.103790
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95   0.100917
##                                                               t value Pr(>|t|)
## (Intercept)                                                    44.962  < 2e-16
## dur_target                                                      4.184 2.87e-05
## factor(q_dur_distractor)0.25                                   -0.753 0.451433
## factor(q_dur_distractor)0.5                                    -3.683 0.000231
## factor(q_dur_distractor)0.75                                   -2.573 0.010071
## factor(condition)0.3                                            0.757 0.448808
## factor(condition)0.5                                            0.353 0.723719
## factor(condition)0.7                                            1.810 0.070336
## factor(condition)0.85                                          -0.184 0.853690
## factor(condition)0.95                                          -3.304 0.000952
## dur_target:factor(q_dur_distractor)0.25                        -1.388 0.165053
## dur_target:factor(q_dur_distractor)0.5                         -0.202 0.839909
## dur_target:factor(q_dur_distractor)0.75                        -2.422 0.015431
## dur_target:factor(condition)0.3                                -0.730 0.465419
## dur_target:factor(condition)0.5                                 1.244 0.213347
## dur_target:factor(condition)0.7                                 0.928 0.353448
## dur_target:factor(condition)0.85                                1.697 0.089721
## dur_target:factor(condition)0.95                                2.475 0.013317
## factor(q_dur_distractor)0.25:factor(condition)0.3              -0.420 0.674775
## factor(q_dur_distractor)0.5:factor(condition)0.3               -1.092 0.274669
## factor(q_dur_distractor)0.75:factor(condition)0.3               0.132 0.894778
## factor(q_dur_distractor)0.25:factor(condition)0.5               0.701 0.483496
## factor(q_dur_distractor)0.5:factor(condition)0.5               -0.618 0.536906
## factor(q_dur_distractor)0.75:factor(condition)0.5              -2.650 0.008047
## factor(q_dur_distractor)0.25:factor(condition)0.7              -1.414 0.157511
## factor(q_dur_distractor)0.5:factor(condition)0.7               -2.963 0.003047
## factor(q_dur_distractor)0.75:factor(condition)0.7              -6.130 8.83e-10
## factor(q_dur_distractor)0.25:factor(condition)0.85             -0.592 0.553787
## factor(q_dur_distractor)0.5:factor(condition)0.85              -3.065 0.002178
## factor(q_dur_distractor)0.75:factor(condition)0.85             -3.051 0.002279
## factor(q_dur_distractor)0.25:factor(condition)0.95             -1.119 0.263073
## factor(q_dur_distractor)0.5:factor(condition)0.95              -0.537 0.591561
## factor(q_dur_distractor)0.75:factor(condition)0.95             -4.632 3.62e-06
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3    0.738 0.460805
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3     1.504 0.132488
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3   -0.310 0.756487
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5   -0.911 0.362409
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5    -1.198 0.230846
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5    1.152 0.249521
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7    1.249 0.211683
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7     1.160 0.245949
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7    2.247 0.024620
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85   0.646 0.518427
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85    0.962 0.336270
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85  -0.044 0.964890
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95   1.169 0.242595
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95   -1.320 0.186969
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95   1.355 0.175274
##                                                                  
## (Intercept)                                                   ***
## dur_target                                                    ***
## factor(q_dur_distractor)0.25                                     
## factor(q_dur_distractor)0.5                                   ***
## factor(q_dur_distractor)0.75                                  *  
## factor(condition)0.3                                             
## factor(condition)0.5                                             
## factor(condition)0.7                                          .  
## factor(condition)0.85                                            
## factor(condition)0.95                                         ***
## dur_target:factor(q_dur_distractor)0.25                          
## dur_target:factor(q_dur_distractor)0.5                           
## dur_target:factor(q_dur_distractor)0.75                       *  
## dur_target:factor(condition)0.3                                  
## dur_target:factor(condition)0.5                                  
## dur_target:factor(condition)0.7                                  
## dur_target:factor(condition)0.85                              .  
## dur_target:factor(condition)0.95                              *  
## factor(q_dur_distractor)0.25:factor(condition)0.3                
## factor(q_dur_distractor)0.5:factor(condition)0.3                 
## factor(q_dur_distractor)0.75:factor(condition)0.3                
## factor(q_dur_distractor)0.25:factor(condition)0.5                
## factor(q_dur_distractor)0.5:factor(condition)0.5                 
## factor(q_dur_distractor)0.75:factor(condition)0.5             ** 
## factor(q_dur_distractor)0.25:factor(condition)0.7                
## factor(q_dur_distractor)0.5:factor(condition)0.7              ** 
## factor(q_dur_distractor)0.75:factor(condition)0.7             ***
## factor(q_dur_distractor)0.25:factor(condition)0.85               
## factor(q_dur_distractor)0.5:factor(condition)0.85             ** 
## factor(q_dur_distractor)0.75:factor(condition)0.85            ** 
## factor(q_dur_distractor)0.25:factor(condition)0.95               
## factor(q_dur_distractor)0.5:factor(condition)0.95                
## factor(q_dur_distractor)0.75:factor(condition)0.95            ***
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3     
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3      
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3     
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5     
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5      
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5     
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7     
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7      
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7  *  
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85    
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85     
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85    
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95    
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95     
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.167809)
## 
##     Null deviance: 10853  on 62108  degrees of freedom
## Residual deviance: 10414  on 62061  degrees of freedom
## AIC: 65448
## 
## Number of Fisher Scoring iterations: 2
Anova(f12)
## Analysis of Deviance Table (Type II tests)
## 
## Response: corr
##                                                       LR Chisq Df Pr(>Chisq)
## dur_target                                             1046.09  1  < 2.2e-16
## factor(q_dur_distractor)                               1444.03  3  < 2.2e-16
## factor(condition)                                       200.48  5  < 2.2e-16
## dur_target:factor(q_dur_distractor)                      28.73  3  2.546e-06
## dur_target:factor(condition)                            114.60  5  < 2.2e-16
## factor(q_dur_distractor):factor(condition)              134.59 15  < 2.2e-16
## dur_target:factor(q_dur_distractor):factor(condition)    58.92 15  3.868e-07
##                                                          
## dur_target                                            ***
## factor(q_dur_distractor)                              ***
## factor(condition)                                     ***
## dur_target:factor(q_dur_distractor)                   ***
## dur_target:factor(condition)                          ***
## factor(q_dur_distractor):factor(condition)            ***
## dur_target:factor(q_dur_distractor):factor(condition) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f12, terms = c("dur_target", "q_dur_distractor", "condition")))

dur_target, dur_distractorの両者で標準化された確信度を説明

hist(dat$dur_target)

hist(dat$dur_distractor)

hist(dat$conf_normalized)

# condition aggregated
ggplot(subset(dat, dat$conf_normalized > -3), aes(x = dur_target, y = conf_normalized, color = factor(q_dur_distractor))) + 
    geom_count(alpha = 0.5) + stat_smooth(size = 1.2) +
    scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + ylim(-3, 3) + facet_wrap(. ~ chosenItem)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: Removed 2611 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2611 rows containing non-finite values (`stat_smooth()`).

f13 <- lm(conf_normalized ~ dur_target * dur_distractor * chosenItem, 
         data = subset(dat, dat$conf_normalized > -3 & 
                           dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f13)
## 
## Call:
## lm(formula = conf_normalized ~ dur_target * dur_distractor * 
##     chosenItem, data = subset(dat, dat$conf_normalized > -3 & 
##     dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9657 -0.6660  0.1154  0.6418  3.1615 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                     0.16434    0.01275  12.888
## dur_target                                      0.22178    0.04789   4.631
## dur_distractor                                  0.07545    0.05235   1.441
## chosenItemdistractor                           -0.27698    0.02515 -11.013
## dur_target:dur_distractor                      -1.45513    0.13931 -10.445
## dur_target:chosenItemdistractor                -1.17246    0.10255 -11.433
## dur_distractor:chosenItemdistractor            -0.70288    0.10229  -6.872
## dur_target:dur_distractor:chosenItemdistractor  2.33020    0.26518   8.787
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## dur_target                                     3.64e-06 ***
## dur_distractor                                     0.15    
## chosenItemdistractor                            < 2e-16 ***
## dur_target:dur_distractor                       < 2e-16 ***
## dur_target:chosenItemdistractor                 < 2e-16 ***
## dur_distractor:chosenItemdistractor            6.42e-12 ***
## dur_target:dur_distractor:chosenItemdistractor  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9604 on 55453 degrees of freedom
## Multiple R-squared:  0.06573,    Adjusted R-squared:  0.06561 
## F-statistic: 557.3 on 7 and 55453 DF,  p-value: < 2.2e-16
Anova(f13)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                      Sum Sq    Df   F value    Pr(>F)    
## dur_target                               99     1  107.4099 < 2.2e-16 ***
## dur_distractor                          183     1  198.8686 < 2.2e-16 ***
## chosenItem                             2723     1 2952.3154 < 2.2e-16 ***
## dur_target:dur_distractor                43     1   46.9304 7.432e-12 ***
## dur_target:chosenItem                    49     1   53.5044 2.615e-13 ***
## dur_distractor:chosenItem                 0     1    0.0072    0.9323    
## dur_target:dur_distractor:chosenItem     71     1   77.2143 < 2.2e-16 ***
## Residuals                             51151 55453                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f13, terms = c("dur_target", "dur_distractor", "chosenItem")))

f14 <- lm(conf_normalized ~ dur_target * factor(q_dur_distractor) * chosenItem, 
         data = subset(dat, dat$conf_normalized > -3 & 
                           dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f14)
## 
## Call:
## lm(formula = conf_normalized ~ dur_target * factor(q_dur_distractor) * 
##     chosenItem, data = subset(dat, dat$conf_normalized > -3 & 
##     dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9893 -0.6845  0.1217  0.6611  3.0949 
## 
## Coefficients:
##                                                                Estimate
## (Intercept)                                                   0.2117361
## dur_target                                                    0.1031362
## factor(q_dur_distractor)0.25                                  0.0006503
## factor(q_dur_distractor)0.5                                  -0.0572586
## factor(q_dur_distractor)0.75                                 -0.0050727
## chosenItemdistractor                                         -0.5005380
## dur_target:factor(q_dur_distractor)0.25                      -0.2477491
## dur_target:factor(q_dur_distractor)0.5                       -0.2771062
## dur_target:factor(q_dur_distractor)0.75                      -0.7915875
## dur_target:chosenItemdistractor                              -0.6119851
## factor(q_dur_distractor)0.25:chosenItemdistractor             0.0155866
## factor(q_dur_distractor)0.5:chosenItemdistractor              0.2452671
## factor(q_dur_distractor)0.75:chosenItemdistractor             0.0174391
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor  0.2219137
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor  -0.3267649
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor  0.3912927
##                                                              Std. Error t value
## (Intercept)                                                   0.0147487  14.356
## dur_target                                                    0.0567846   1.816
## factor(q_dur_distractor)0.25                                  0.0225479   0.029
## factor(q_dur_distractor)0.5                                   0.0227952  -2.512
## factor(q_dur_distractor)0.75                                  0.0235686  -0.215
## chosenItemdistractor                                          0.0330045 -15.166
## dur_target:factor(q_dur_distractor)0.25                       0.0792361  -3.127
## dur_target:factor(q_dur_distractor)0.5                        0.0774608  -3.577
## dur_target:factor(q_dur_distractor)0.75                       0.0757519 -10.450
## dur_target:chosenItemdistractor                               0.1529327  -4.002
## factor(q_dur_distractor)0.25:chosenItemdistractor             0.0496037   0.314
## factor(q_dur_distractor)0.5:chosenItemdistractor              0.0452469   5.421
## factor(q_dur_distractor)0.75:chosenItemdistractor             0.0456319   0.382
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor  0.2070724   1.072
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor   0.1865373  -1.752
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor  0.1779828   2.198
##                                                              Pr(>|t|)    
## (Intercept)                                                   < 2e-16 ***
## dur_target                                                   0.069334 .  
## factor(q_dur_distractor)0.25                                 0.976991    
## factor(q_dur_distractor)0.5                                  0.012012 *  
## factor(q_dur_distractor)0.75                                 0.829587    
## chosenItemdistractor                                          < 2e-16 ***
## dur_target:factor(q_dur_distractor)0.25                      0.001769 ** 
## dur_target:factor(q_dur_distractor)0.5                       0.000347 ***
## dur_target:factor(q_dur_distractor)0.75                       < 2e-16 ***
## dur_target:chosenItemdistractor                              6.30e-05 ***
## factor(q_dur_distractor)0.25:chosenItemdistractor            0.753353    
## factor(q_dur_distractor)0.5:chosenItemdistractor             5.96e-08 ***
## factor(q_dur_distractor)0.75:chosenItemdistractor            0.702338    
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.283872    
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor  0.079824 .  
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.027919 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9592 on 55445 degrees of freedom
## Multiple R-squared:  0.06831,    Adjusted R-squared:  0.06806 
## F-statistic:   271 on 15 and 55445 DF,  p-value: < 2.2e-16
Anova(f14)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                Sum Sq    Df   F value    Pr(>F)
## dur_target                                        248     1  269.8508 < 2.2e-16
## factor(q_dur_distractor)                          262     3   94.8801 < 2.2e-16
## chosenItem                                       2647     1 2876.8279 < 2.2e-16
## dur_target:factor(q_dur_distractor)               105     3   37.9460 < 2.2e-16
## dur_target:chosenItem                              73     1   79.5544 < 2.2e-16
## factor(q_dur_distractor):chosenItem                31     3   11.2748 2.168e-07
## dur_target:factor(q_dur_distractor):chosenItem     25     3    9.1304 4.889e-06
## Residuals                                       51010 55445                    
##                                                   
## dur_target                                     ***
## factor(q_dur_distractor)                       ***
## chosenItem                                     ***
## dur_target:factor(q_dur_distractor)            ***
## dur_target:chosenItem                          ***
## factor(q_dur_distractor):chosenItem            ***
## dur_target:factor(q_dur_distractor):chosenItem ***
## Residuals                                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f14, terms = c("dur_target", "q_dur_distractor", "chosenItem")))

# condition separated
ggplot(subset(dat, dat$conf_normalized > -3 & dat$chosenItem != "dud"), aes(x = dur_target, y = conf_normalized, color = factor(q_dur_distractor))) + 
    geom_count(alpha = 0.5) + stat_smooth(size = 1.2) +
    scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + ylim(-3, 3) + facet_nested(. ~ chosenItem + condition)
## Warning: Removed 2595 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2595 rows containing non-finite values (`stat_smooth()`).

f15 <- lm(conf_normalized ~ dur_target * dur_distractor * chosenItem * factor(condition), 
          data = subset(dat, dat$conf_normalized > -3 & 
                            dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f15)
## 
## Call:
## lm(formula = conf_normalized ~ dur_target * dur_distractor * 
##     chosenItem * factor(condition), data = subset(dat, dat$conf_normalized > 
##     -3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1480 -0.6892  0.1239  0.7156  3.2792 
## 
## Coefficients:
##                                                                       Estimate
## (Intercept)                                                           0.152589
## dur_target                                                            0.393589
## dur_distractor                                                        0.032907
## chosenItemdistractor                                                 -0.391511
## factor(condition)0.3                                                  0.265257
## factor(condition)0.5                                                  0.213857
## factor(condition)0.7                                                  0.020696
## factor(condition)0.85                                                -0.101493
## factor(condition)0.95                                                -0.247144
## dur_target:dur_distractor                                            -1.729456
## dur_target:chosenItemdistractor                                      -1.615441
## dur_distractor:chosenItemdistractor                                  -0.108707
## dur_target:factor(condition)0.3                                      -0.544545
## dur_target:factor(condition)0.5                                      -0.541084
## dur_target:factor(condition)0.7                                       0.119335
## dur_target:factor(condition)0.85                                      0.028688
## dur_target:factor(condition)0.95                                      0.110702
## dur_distractor:factor(condition)0.3                                   0.015035
## dur_distractor:factor(condition)0.5                                   0.100398
## dur_distractor:factor(condition)0.7                                   0.303708
## dur_distractor:factor(condition)0.85                                 -0.088502
## dur_distractor:factor(condition)0.95                                 -0.681138
## chosenItemdistractor:factor(condition)0.3                             0.141430
## chosenItemdistractor:factor(condition)0.5                             0.154115
## chosenItemdistractor:factor(condition)0.7                             0.244318
## chosenItemdistractor:factor(condition)0.85                           -0.012806
## chosenItemdistractor:factor(condition)0.95                            0.017849
## dur_target:dur_distractor:chosenItemdistractor                        1.716865
## dur_target:dur_distractor:factor(condition)0.3                        0.762466
## dur_target:dur_distractor:factor(condition)0.5                        0.687460
## dur_target:dur_distractor:factor(condition)0.7                       -1.065963
## dur_target:dur_distractor:factor(condition)0.85                       0.177283
## dur_target:dur_distractor:factor(condition)0.95                       0.729956
## dur_target:chosenItemdistractor:factor(condition)0.3                  0.687209
## dur_target:chosenItemdistractor:factor(condition)0.5                  0.185473
## dur_target:chosenItemdistractor:factor(condition)0.7                  0.426613
## dur_target:chosenItemdistractor:factor(condition)0.85                 0.234246
## dur_target:chosenItemdistractor:factor(condition)0.95                 0.832629
## dur_distractor:chosenItemdistractor:factor(condition)0.3             -0.587589
## dur_distractor:chosenItemdistractor:factor(condition)0.5             -1.224366
## dur_distractor:chosenItemdistractor:factor(condition)0.7             -1.126309
## dur_distractor:chosenItemdistractor:factor(condition)0.85            -0.032058
## dur_distractor:chosenItemdistractor:factor(condition)0.95             0.397322
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3   0.003624
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5   2.507014
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7   2.090784
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85  0.655318
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 -1.439353
##                                                                      Std. Error
## (Intercept)                                                            0.034208
## dur_target                                                             0.117228
## dur_distractor                                                         0.120425
## chosenItemdistractor                                                   0.065989
## factor(condition)0.3                                                   0.046789
## factor(condition)0.5                                                   0.046042
## factor(condition)0.7                                                   0.045555
## factor(condition)0.85                                                  0.045193
## factor(condition)0.95                                                  0.045431
## dur_target:dur_distractor                                              0.301644
## dur_target:chosenItemdistractor                                        0.241943
## dur_distractor:chosenItemdistractor                                    0.243158
## dur_target:factor(condition)0.3                                        0.169995
## dur_target:factor(condition)0.5                                        0.164764
## dur_target:factor(condition)0.7                                        0.167722
## dur_target:factor(condition)0.85                                       0.162087
## dur_target:factor(condition)0.95                                       0.162991
## dur_distractor:factor(condition)0.3                                    0.170765
## dur_distractor:factor(condition)0.5                                    0.174232
## dur_distractor:factor(condition)0.7                                    0.179765
## dur_distractor:factor(condition)0.85                                   0.180007
## dur_distractor:factor(condition)0.95                                   0.182201
## chosenItemdistractor:factor(condition)0.3                              0.088741
## chosenItemdistractor:factor(condition)0.5                              0.089845
## chosenItemdistractor:factor(condition)0.7                              0.090032
## chosenItemdistractor:factor(condition)0.85                             0.088377
## chosenItemdistractor:factor(condition)0.95                             0.092938
## dur_target:dur_distractor:chosenItemdistractor                         0.587224
## dur_target:dur_distractor:factor(condition)0.3                         0.447612
## dur_target:dur_distractor:factor(condition)0.5                         0.447423
## dur_target:dur_distractor:factor(condition)0.7                         0.473620
## dur_target:dur_distractor:factor(condition)0.85                        0.478851
## dur_target:dur_distractor:factor(condition)0.95                        0.485916
## dur_target:chosenItemdistractor:factor(condition)0.3                   0.335010
## dur_target:chosenItemdistractor:factor(condition)0.5                   0.350785
## dur_target:chosenItemdistractor:factor(condition)0.7                   0.375173
## dur_target:chosenItemdistractor:factor(condition)0.85                  0.352899
## dur_target:chosenItemdistractor:factor(condition)0.95                  0.368551
## dur_distractor:chosenItemdistractor:factor(condition)0.3               0.335203
## dur_distractor:chosenItemdistractor:factor(condition)0.5               0.355135
## dur_distractor:chosenItemdistractor:factor(condition)0.7               0.365782
## dur_distractor:chosenItemdistractor:factor(condition)0.85              0.347526
## dur_distractor:chosenItemdistractor:factor(condition)0.95              0.365578
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3    0.790824
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5    0.897349
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7    1.015129
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85   0.931406
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95   1.007507
##                                                                      t value
## (Intercept)                                                            4.461
## dur_target                                                             3.357
## dur_distractor                                                         0.273
## chosenItemdistractor                                                  -5.933
## factor(condition)0.3                                                   5.669
## factor(condition)0.5                                                   4.645
## factor(condition)0.7                                                   0.454
## factor(condition)0.85                                                 -2.246
## factor(condition)0.95                                                 -5.440
## dur_target:dur_distractor                                             -5.733
## dur_target:chosenItemdistractor                                       -6.677
## dur_distractor:chosenItemdistractor                                   -0.447
## dur_target:factor(condition)0.3                                       -3.203
## dur_target:factor(condition)0.5                                       -3.284
## dur_target:factor(condition)0.7                                        0.712
## dur_target:factor(condition)0.85                                       0.177
## dur_target:factor(condition)0.95                                       0.679
## dur_distractor:factor(condition)0.3                                    0.088
## dur_distractor:factor(condition)0.5                                    0.576
## dur_distractor:factor(condition)0.7                                    1.689
## dur_distractor:factor(condition)0.85                                  -0.492
## dur_distractor:factor(condition)0.95                                  -3.738
## chosenItemdistractor:factor(condition)0.3                              1.594
## chosenItemdistractor:factor(condition)0.5                              1.715
## chosenItemdistractor:factor(condition)0.7                              2.714
## chosenItemdistractor:factor(condition)0.85                            -0.145
## chosenItemdistractor:factor(condition)0.95                             0.192
## dur_target:dur_distractor:chosenItemdistractor                         2.924
## dur_target:dur_distractor:factor(condition)0.3                         1.703
## dur_target:dur_distractor:factor(condition)0.5                         1.536
## dur_target:dur_distractor:factor(condition)0.7                        -2.251
## dur_target:dur_distractor:factor(condition)0.85                        0.370
## dur_target:dur_distractor:factor(condition)0.95                        1.502
## dur_target:chosenItemdistractor:factor(condition)0.3                   2.051
## dur_target:chosenItemdistractor:factor(condition)0.5                   0.529
## dur_target:chosenItemdistractor:factor(condition)0.7                   1.137
## dur_target:chosenItemdistractor:factor(condition)0.85                  0.664
## dur_target:chosenItemdistractor:factor(condition)0.95                  2.259
## dur_distractor:chosenItemdistractor:factor(condition)0.3              -1.753
## dur_distractor:chosenItemdistractor:factor(condition)0.5              -3.448
## dur_distractor:chosenItemdistractor:factor(condition)0.7              -3.079
## dur_distractor:chosenItemdistractor:factor(condition)0.85             -0.092
## dur_distractor:chosenItemdistractor:factor(condition)0.95              1.087
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3    0.005
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5    2.794
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7    2.060
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85   0.704
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95  -1.429
##                                                                      Pr(>|t|)
## (Intercept)                                                          8.19e-06
## dur_target                                                           0.000787
## dur_distractor                                                       0.784660
## chosenItemdistractor                                                 2.99e-09
## factor(condition)0.3                                                 1.44e-08
## factor(condition)0.5                                                 3.41e-06
## factor(condition)0.7                                                 0.649610
## factor(condition)0.85                                                0.024723
## factor(condition)0.95                                                5.35e-08
## dur_target:dur_distractor                                            9.89e-09
## dur_target:chosenItemdistractor                                      2.46e-11
## dur_distractor:chosenItemdistractor                                  0.654830
## dur_target:factor(condition)0.3                                      0.001359
## dur_target:factor(condition)0.5                                      0.001024
## dur_target:factor(condition)0.7                                      0.476774
## dur_target:factor(condition)0.85                                     0.859514
## dur_target:factor(condition)0.95                                     0.497022
## dur_distractor:factor(condition)0.3                                  0.929839
## dur_distractor:factor(condition)0.5                                  0.564460
## dur_distractor:factor(condition)0.7                                  0.091135
## dur_distractor:factor(condition)0.85                                 0.622961
## dur_distractor:factor(condition)0.95                                 0.000185
## chosenItemdistractor:factor(condition)0.3                            0.111002
## chosenItemdistractor:factor(condition)0.5                            0.086289
## chosenItemdistractor:factor(condition)0.7                            0.006656
## chosenItemdistractor:factor(condition)0.85                           0.884790
## chosenItemdistractor:factor(condition)0.95                           0.847702
## dur_target:dur_distractor:chosenItemdistractor                       0.003460
## dur_target:dur_distractor:factor(condition)0.3                       0.088498
## dur_target:dur_distractor:factor(condition)0.5                       0.124424
## dur_target:dur_distractor:factor(condition)0.7                       0.024410
## dur_target:dur_distractor:factor(condition)0.85                      0.711215
## dur_target:dur_distractor:factor(condition)0.95                      0.133044
## dur_target:chosenItemdistractor:factor(condition)0.3                 0.040242
## dur_target:chosenItemdistractor:factor(condition)0.5                 0.596990
## dur_target:chosenItemdistractor:factor(condition)0.7                 0.255496
## dur_target:chosenItemdistractor:factor(condition)0.85                0.506837
## dur_target:chosenItemdistractor:factor(condition)0.95                0.023875
## dur_distractor:chosenItemdistractor:factor(condition)0.3             0.079619
## dur_distractor:chosenItemdistractor:factor(condition)0.5             0.000566
## dur_distractor:chosenItemdistractor:factor(condition)0.7             0.002077
## dur_distractor:chosenItemdistractor:factor(condition)0.85            0.926502
## dur_distractor:chosenItemdistractor:factor(condition)0.95            0.277115
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3  0.996344
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5  0.005211
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7  0.039439
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85 0.481697
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 0.153117
##                                                                         
## (Intercept)                                                          ***
## dur_target                                                           ***
## dur_distractor                                                          
## chosenItemdistractor                                                 ***
## factor(condition)0.3                                                 ***
## factor(condition)0.5                                                 ***
## factor(condition)0.7                                                    
## factor(condition)0.85                                                *  
## factor(condition)0.95                                                ***
## dur_target:dur_distractor                                            ***
## dur_target:chosenItemdistractor                                      ***
## dur_distractor:chosenItemdistractor                                     
## dur_target:factor(condition)0.3                                      ** 
## dur_target:factor(condition)0.5                                      ** 
## dur_target:factor(condition)0.7                                         
## dur_target:factor(condition)0.85                                        
## dur_target:factor(condition)0.95                                        
## dur_distractor:factor(condition)0.3                                     
## dur_distractor:factor(condition)0.5                                     
## dur_distractor:factor(condition)0.7                                  .  
## dur_distractor:factor(condition)0.85                                    
## dur_distractor:factor(condition)0.95                                 ***
## chosenItemdistractor:factor(condition)0.3                               
## chosenItemdistractor:factor(condition)0.5                            .  
## chosenItemdistractor:factor(condition)0.7                            ** 
## chosenItemdistractor:factor(condition)0.85                              
## chosenItemdistractor:factor(condition)0.95                              
## dur_target:dur_distractor:chosenItemdistractor                       ** 
## dur_target:dur_distractor:factor(condition)0.3                       .  
## dur_target:dur_distractor:factor(condition)0.5                          
## dur_target:dur_distractor:factor(condition)0.7                       *  
## dur_target:dur_distractor:factor(condition)0.85                         
## dur_target:dur_distractor:factor(condition)0.95                         
## dur_target:chosenItemdistractor:factor(condition)0.3                 *  
## dur_target:chosenItemdistractor:factor(condition)0.5                    
## dur_target:chosenItemdistractor:factor(condition)0.7                    
## dur_target:chosenItemdistractor:factor(condition)0.85                   
## dur_target:chosenItemdistractor:factor(condition)0.95                *  
## dur_distractor:chosenItemdistractor:factor(condition)0.3             .  
## dur_distractor:chosenItemdistractor:factor(condition)0.5             ***
## dur_distractor:chosenItemdistractor:factor(condition)0.7             ** 
## dur_distractor:chosenItemdistractor:factor(condition)0.85               
## dur_distractor:chosenItemdistractor:factor(condition)0.95               
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3     
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5  ** 
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7  *  
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85    
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9449 on 55413 degrees of freedom
## Multiple R-squared:  0.09629,    Adjusted R-squared:  0.09553 
## F-statistic: 125.6 on 47 and 55413 DF,  p-value: < 2.2e-16
Anova(f15)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                        Sum Sq    Df   F value
## dur_target                                                 89     1   99.2917
## dur_distractor                                            250     1  280.3116
## chosenItem                                               2680     1 3001.2125
## factor(condition)                                        1471     5  329.4529
## dur_target:dur_distractor                                  47     1   52.3358
## dur_target:chosenItem                                      62     1   69.1842
## dur_distractor:chosenItem                                   6     1    6.5299
## dur_target:factor(condition)                               48     5   10.7546
## dur_distractor:factor(condition)                           55     5   12.3967
## chosenItem:factor(condition)                               56     5   12.5547
## dur_target:dur_distractor:chosenItem                       63     1   70.2299
## dur_target:dur_distractor:factor(condition)                21     5    4.6015
## dur_target:chosenItem:factor(condition)                    25     5    5.5651
## dur_distractor:chosenItem:factor(condition)                20     5    4.5739
## dur_target:dur_distractor:chosenItem:factor(condition)     18     5    3.9291
## Residuals                                               49478 55413          
##                                                           Pr(>F)    
## dur_target                                             < 2.2e-16 ***
## dur_distractor                                         < 2.2e-16 ***
## chosenItem                                             < 2.2e-16 ***
## factor(condition)                                      < 2.2e-16 ***
## dur_target:dur_distractor                              4.738e-13 ***
## dur_target:chosenItem                                  < 2.2e-16 ***
## dur_distractor:chosenItem                              0.0106102 *  
## dur_target:factor(condition)                           2.361e-10 ***
## dur_distractor:factor(condition)                       4.801e-12 ***
## chosenItem:factor(condition)                           3.296e-12 ***
## dur_target:dur_distractor:chosenItem                   < 2.2e-16 ***
## dur_target:dur_distractor:factor(condition)            0.0003370 ***
## dur_target:chosenItem:factor(condition)                3.948e-05 ***
## dur_distractor:chosenItem:factor(condition)            0.0003582 ***
## dur_target:dur_distractor:chosenItem:factor(condition) 0.0014582 ** 
## Residuals                                                           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f15, terms = c("dur_target", "dur_distractor", "chosenItem", "condition")))

f16 <- lm(conf_normalized ~ dur_target * factor(q_dur_distractor) * chosenItem * factor(condition), 
          data = subset(dat, dat$conf_normalized > -3 & 
                            dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f16)
## 
## Call:
## lm(formula = conf_normalized ~ dur_target * factor(q_dur_distractor) * 
##     chosenItem * factor(condition), data = subset(dat, dat$conf_normalized > 
##     -3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1915 -0.6923  0.1254  0.7321  3.1605 
## 
## Coefficients:
##                                                                                     Estimate
## (Intercept)                                                                         0.223556
## dur_target                                                                          0.231218
## factor(q_dur_distractor)0.25                                                        0.017234
## factor(q_dur_distractor)0.5                                                        -0.033788
## factor(q_dur_distractor)0.75                                                       -0.064629
## chosenItemdistractor                                                               -0.592958
## factor(condition)0.3                                                                0.215666
## factor(condition)0.5                                                                0.145828
## factor(condition)0.7                                                                0.085599
## factor(condition)0.85                                                              -0.093410
## factor(condition)0.95                                                              -0.359643
## dur_target:factor(q_dur_distractor)0.25                                            -0.432461
## dur_target:factor(q_dur_distractor)0.5                                             -0.347522
## dur_target:factor(q_dur_distractor)0.75                                            -0.915591
## dur_target:chosenItemdistractor                                                    -0.910884
## factor(q_dur_distractor)0.25:chosenItemdistractor                                   0.037269
## factor(q_dur_distractor)0.5:chosenItemdistractor                                    0.214572
## factor(q_dur_distractor)0.75:chosenItemdistractor                                   0.218474
## dur_target:factor(condition)0.3                                                    -0.287335
## dur_target:factor(condition)0.5                                                    -0.099180
## dur_target:factor(condition)0.7                                                    -0.232420
## dur_target:factor(condition)0.85                                                   -0.050718
## dur_target:factor(condition)0.95                                                    0.241256
## factor(q_dur_distractor)0.25:factor(condition)0.3                                  -0.070835
## factor(q_dur_distractor)0.5:factor(condition)0.3                                    0.081544
## factor(q_dur_distractor)0.75:factor(condition)0.3                                   0.038657
## factor(q_dur_distractor)0.25:factor(condition)0.5                                   0.045339
## factor(q_dur_distractor)0.5:factor(condition)0.5                                    0.083681
## factor(q_dur_distractor)0.75:factor(condition)0.5                                   0.047860
## factor(q_dur_distractor)0.25:factor(condition)0.7                                  -0.135422
## factor(q_dur_distractor)0.5:factor(condition)0.7                                   -0.098963
## factor(q_dur_distractor)0.75:factor(condition)0.7                                   0.159849
## factor(q_dur_distractor)0.25:factor(condition)0.85                                 -0.098060
## factor(q_dur_distractor)0.5:factor(condition)0.85                                  -0.163805
## factor(q_dur_distractor)0.75:factor(condition)0.85                                  0.053643
## factor(q_dur_distractor)0.25:factor(condition)0.95                                  0.118454
## factor(q_dur_distractor)0.5:factor(condition)0.95                                  -0.077289
## factor(q_dur_distractor)0.75:factor(condition)0.95                                 -0.101110
## chosenItemdistractor:factor(condition)0.3                                           0.211186
## chosenItemdistractor:factor(condition)0.5                                           0.040706
## chosenItemdistractor:factor(condition)0.7                                           0.251905
## chosenItemdistractor:factor(condition)0.85                                         -0.122312
## chosenItemdistractor:factor(condition)0.95                                          0.130622
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor                        0.290576
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor                        -0.654354
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor                        0.264103
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3                        0.165689
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3                        -0.046161
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3                        0.216786
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5                       -0.216620
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5                        -0.329261
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5                        0.152905
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7                        0.619141
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7                         0.101614
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7                       -0.345307
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85                       0.318844
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85                        0.377570
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85                      -0.124471
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95                      -0.129717
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95                       -0.491945
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95                       0.227690
## dur_target:chosenItemdistractor:factor(condition)0.3                               -0.084827
## dur_target:chosenItemdistractor:factor(condition)0.5                                0.055463
## dur_target:chosenItemdistractor:factor(condition)0.7                                0.169597
## dur_target:chosenItemdistractor:factor(condition)0.85                               0.833248
## dur_target:chosenItemdistractor:factor(condition)0.95                               0.448712
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3              0.035184
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3              -0.203738
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3             -0.306369
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5              0.127348
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5              -0.067986
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5             -0.276096
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7             -0.073422
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7              -0.158742
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7             -0.409466
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85            -0.043762
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85              0.473127
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85            -0.001868
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95            -0.253357
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95              0.154311
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95            -0.034956
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3  -0.034721
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3    1.472245
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3   0.457927
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5  -0.356454
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5    1.183369
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5   0.726738
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7  -0.221615
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7    1.007585
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7   0.898216
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85  0.217956
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85  -1.409832
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 -0.081669
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95  0.973096
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95   0.475799
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 -0.736355
##                                                                                    Std. Error
## (Intercept)                                                                          0.046291
## dur_target                                                                           0.179126
## factor(q_dur_distractor)0.25                                                         0.064666
## factor(q_dur_distractor)0.5                                                          0.059877
## factor(q_dur_distractor)0.75                                                         0.060126
## chosenItemdistractor                                                                 0.099328
## factor(condition)0.3                                                                 0.060811
## factor(condition)0.5                                                                 0.058990
## factor(condition)0.7                                                                 0.057062
## factor(condition)0.85                                                                0.056326
## factor(condition)0.95                                                                0.056008
## dur_target:factor(q_dur_distractor)0.25                                              0.222154
## dur_target:factor(q_dur_distractor)0.5                                               0.207899
## dur_target:factor(q_dur_distractor)0.75                                              0.205040
## dur_target:chosenItemdistractor                                                      0.441662
## factor(q_dur_distractor)0.25:chosenItemdistractor                                    0.138975
## factor(q_dur_distractor)0.5:chosenItemdistractor                                     0.124154
## factor(q_dur_distractor)0.75:chosenItemdistractor                                    0.123206
## dur_target:factor(condition)0.3                                                      0.245744
## dur_target:factor(condition)0.5                                                      0.231151
## dur_target:factor(condition)0.7                                                      0.224252
## dur_target:factor(condition)0.85                                                     0.213967
## dur_target:factor(condition)0.95                                                     0.212110
## factor(q_dur_distractor)0.25:factor(condition)0.3                                    0.086654
## factor(q_dur_distractor)0.5:factor(condition)0.3                                     0.084045
## factor(q_dur_distractor)0.75:factor(condition)0.3                                    0.082460
## factor(q_dur_distractor)0.25:factor(condition)0.5                                    0.084612
## factor(q_dur_distractor)0.5:factor(condition)0.5                                     0.081842
## factor(q_dur_distractor)0.75:factor(condition)0.5                                    0.083043
## factor(q_dur_distractor)0.25:factor(condition)0.7                                    0.082723
## factor(q_dur_distractor)0.5:factor(condition)0.7                                     0.079608
## factor(q_dur_distractor)0.75:factor(condition)0.7                                    0.084492
## factor(q_dur_distractor)0.25:factor(condition)0.85                                   0.082234
## factor(q_dur_distractor)0.5:factor(condition)0.85                                    0.081642
## factor(q_dur_distractor)0.75:factor(condition)0.85                                   0.083937
## factor(q_dur_distractor)0.25:factor(condition)0.95                                   0.082921
## factor(q_dur_distractor)0.5:factor(condition)0.95                                    0.080497
## factor(q_dur_distractor)0.75:factor(condition)0.95                                   0.083808
## chosenItemdistractor:factor(condition)0.3                                            0.128129
## chosenItemdistractor:factor(condition)0.5                                            0.126091
## chosenItemdistractor:factor(condition)0.7                                            0.126703
## chosenItemdistractor:factor(condition)0.85                                           0.121746
## chosenItemdistractor:factor(condition)0.95                                           0.127465
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor                         0.548733
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor                          0.503543
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor                         0.483339
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3                         0.309156
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3                          0.299989
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3                         0.286842
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5                         0.298857
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5                          0.283407
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5                         0.278439
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7                         0.292302
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7                          0.275636
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7                         0.281156
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85                        0.282496
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85                         0.280028
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85                        0.277505
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95                        0.284016
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95                         0.277561
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95                        0.270381
## dur_target:chosenItemdistractor:factor(condition)0.3                                 0.568313
## dur_target:chosenItemdistractor:factor(condition)0.5                                 0.581960
## dur_target:chosenItemdistractor:factor(condition)0.7                                 0.608711
## dur_target:chosenItemdistractor:factor(condition)0.85                                0.537326
## dur_target:chosenItemdistractor:factor(condition)0.95                                0.582734
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3               0.182374
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3                0.170034
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3               0.164694
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5               0.182623
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5                0.162618
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5               0.164189
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7               0.180433
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7                0.163839
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7               0.165458
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85              0.180329
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85               0.161587
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85              0.162678
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95              0.184953
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95               0.165877
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95              0.167553
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3    0.721095
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3     0.685843
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3    0.632029
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5    0.743645
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5     0.668697
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5    0.652803
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7    0.781753
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7     0.709184
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7    0.683981
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85   0.731626
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85    0.654292
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85   0.622147
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95   0.761565
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95    0.684138
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95   0.664687
##                                                                                    t value
## (Intercept)                                                                          4.829
## dur_target                                                                           1.291
## factor(q_dur_distractor)0.25                                                         0.267
## factor(q_dur_distractor)0.5                                                         -0.564
## factor(q_dur_distractor)0.75                                                        -1.075
## chosenItemdistractor                                                                -5.970
## factor(condition)0.3                                                                 3.547
## factor(condition)0.5                                                                 2.472
## factor(condition)0.7                                                                 1.500
## factor(condition)0.85                                                               -1.658
## factor(condition)0.95                                                               -6.421
## dur_target:factor(q_dur_distractor)0.25                                             -1.947
## dur_target:factor(q_dur_distractor)0.5                                              -1.672
## dur_target:factor(q_dur_distractor)0.75                                             -4.465
## dur_target:chosenItemdistractor                                                     -2.062
## factor(q_dur_distractor)0.25:chosenItemdistractor                                    0.268
## factor(q_dur_distractor)0.5:chosenItemdistractor                                     1.728
## factor(q_dur_distractor)0.75:chosenItemdistractor                                    1.773
## dur_target:factor(condition)0.3                                                     -1.169
## dur_target:factor(condition)0.5                                                     -0.429
## dur_target:factor(condition)0.7                                                     -1.036
## dur_target:factor(condition)0.85                                                    -0.237
## dur_target:factor(condition)0.95                                                     1.137
## factor(q_dur_distractor)0.25:factor(condition)0.3                                   -0.817
## factor(q_dur_distractor)0.5:factor(condition)0.3                                     0.970
## factor(q_dur_distractor)0.75:factor(condition)0.3                                    0.469
## factor(q_dur_distractor)0.25:factor(condition)0.5                                    0.536
## factor(q_dur_distractor)0.5:factor(condition)0.5                                     1.022
## factor(q_dur_distractor)0.75:factor(condition)0.5                                    0.576
## factor(q_dur_distractor)0.25:factor(condition)0.7                                   -1.637
## factor(q_dur_distractor)0.5:factor(condition)0.7                                    -1.243
## factor(q_dur_distractor)0.75:factor(condition)0.7                                    1.892
## factor(q_dur_distractor)0.25:factor(condition)0.85                                  -1.192
## factor(q_dur_distractor)0.5:factor(condition)0.85                                   -2.006
## factor(q_dur_distractor)0.75:factor(condition)0.85                                   0.639
## factor(q_dur_distractor)0.25:factor(condition)0.95                                   1.429
## factor(q_dur_distractor)0.5:factor(condition)0.95                                   -0.960
## factor(q_dur_distractor)0.75:factor(condition)0.95                                  -1.206
## chosenItemdistractor:factor(condition)0.3                                            1.648
## chosenItemdistractor:factor(condition)0.5                                            0.323
## chosenItemdistractor:factor(condition)0.7                                            1.988
## chosenItemdistractor:factor(condition)0.85                                          -1.005
## chosenItemdistractor:factor(condition)0.95                                           1.025
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor                         0.530
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor                         -1.300
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor                         0.546
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3                         0.536
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3                         -0.154
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3                         0.756
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5                        -0.725
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5                         -1.162
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5                         0.549
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7                         2.118
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7                          0.369
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7                        -1.228
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85                        1.129
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85                         1.348
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85                       -0.449
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95                       -0.457
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95                        -1.772
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95                        0.842
## dur_target:chosenItemdistractor:factor(condition)0.3                                -0.149
## dur_target:chosenItemdistractor:factor(condition)0.5                                 0.095
## dur_target:chosenItemdistractor:factor(condition)0.7                                 0.279
## dur_target:chosenItemdistractor:factor(condition)0.85                                1.551
## dur_target:chosenItemdistractor:factor(condition)0.95                                0.770
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3               0.193
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3               -1.198
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3              -1.860
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5               0.697
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5               -0.418
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5              -1.682
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7              -0.407
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7               -0.969
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7              -2.475
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85             -0.243
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85               2.928
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85             -0.011
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95             -1.370
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95               0.930
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95             -0.209
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3   -0.048
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3     2.147
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3    0.725
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5   -0.479
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5     1.770
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5    1.113
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7   -0.283
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7     1.421
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7    1.313
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85   0.298
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85   -2.155
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85  -0.131
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95   1.278
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95    0.695
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95  -1.108
##                                                                                    Pr(>|t|)
## (Intercept)                                                                        1.37e-06
## dur_target                                                                         0.196775
## factor(q_dur_distractor)0.25                                                       0.789849
## factor(q_dur_distractor)0.5                                                        0.572551
## factor(q_dur_distractor)0.75                                                       0.282427
## chosenItemdistractor                                                               2.39e-09
## factor(condition)0.3                                                               0.000391
## factor(condition)0.5                                                               0.013436
## factor(condition)0.7                                                               0.133596
## factor(condition)0.85                                                              0.097246
## factor(condition)0.95                                                              1.36e-10
## dur_target:factor(q_dur_distractor)0.25                                            0.051580
## dur_target:factor(q_dur_distractor)0.5                                             0.094610
## dur_target:factor(q_dur_distractor)0.75                                            8.01e-06
## dur_target:chosenItemdistractor                                                    0.039174
## factor(q_dur_distractor)0.25:chosenItemdistractor                                  0.788568
## factor(q_dur_distractor)0.5:chosenItemdistractor                                   0.083946
## factor(q_dur_distractor)0.75:chosenItemdistractor                                  0.076194
## dur_target:factor(condition)0.3                                                    0.242309
## dur_target:factor(condition)0.5                                                    0.667874
## dur_target:factor(condition)0.7                                                    0.300009
## dur_target:factor(condition)0.85                                                   0.812629
## dur_target:factor(condition)0.95                                                   0.255372
## factor(q_dur_distractor)0.25:factor(condition)0.3                                  0.413680
## factor(q_dur_distractor)0.5:factor(condition)0.3                                   0.331929
## factor(q_dur_distractor)0.75:factor(condition)0.3                                  0.639221
## factor(q_dur_distractor)0.25:factor(condition)0.5                                  0.592068
## factor(q_dur_distractor)0.5:factor(condition)0.5                                   0.306563
## factor(q_dur_distractor)0.75:factor(condition)0.5                                  0.564394
## factor(q_dur_distractor)0.25:factor(condition)0.7                                  0.101624
## factor(q_dur_distractor)0.5:factor(condition)0.7                                   0.213826
## factor(q_dur_distractor)0.75:factor(condition)0.7                                  0.058513
## factor(q_dur_distractor)0.25:factor(condition)0.85                                 0.233089
## factor(q_dur_distractor)0.5:factor(condition)0.85                                  0.044821
## factor(q_dur_distractor)0.75:factor(condition)0.85                                 0.522764
## factor(q_dur_distractor)0.25:factor(condition)0.95                                 0.153148
## factor(q_dur_distractor)0.5:factor(condition)0.95                                  0.336988
## factor(q_dur_distractor)0.75:factor(condition)0.95                                 0.227648
## chosenItemdistractor:factor(condition)0.3                                          0.099312
## chosenItemdistractor:factor(condition)0.5                                          0.746822
## chosenItemdistractor:factor(condition)0.7                                          0.046800
## chosenItemdistractor:factor(condition)0.85                                         0.315070
## chosenItemdistractor:factor(condition)0.95                                         0.305477
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor                       0.596433
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor                        0.193778
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor                       0.584783
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3                       0.592002
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3                        0.877709
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3                       0.449790
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5                       0.468560
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5                        0.245324
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5                       0.582904
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7                       0.034166
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7                        0.712389
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7                       0.219390
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85                      0.259044
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85                       0.177559
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85                      0.653767
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95                      0.647871
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95                       0.076336
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95                      0.399730
## dur_target:chosenItemdistractor:factor(condition)0.3                               0.881348
## dur_target:chosenItemdistractor:factor(condition)0.5                               0.924073
## dur_target:chosenItemdistractor:factor(condition)0.7                               0.780540
## dur_target:chosenItemdistractor:factor(condition)0.85                              0.120972
## dur_target:chosenItemdistractor:factor(condition)0.95                              0.441296
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3             0.847019
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3              0.230835
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3             0.062858
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5             0.485602
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5              0.675895
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5             0.092657
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7             0.684068
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7              0.332605
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7             0.013336
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85            0.808256
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85             0.003413
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85            0.990839
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95            0.170740
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95             0.352232
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95            0.834739
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3  0.961597
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3   0.031828
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3  0.468741
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5  0.631703
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5   0.076789
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5  0.265603
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7  0.776806
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7   0.155390
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7  0.189115
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.765776
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85  0.031186
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 0.895563
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.201340
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95  0.486762
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 0.267944
##                                                                                       
## (Intercept)                                                                        ***
## dur_target                                                                            
## factor(q_dur_distractor)0.25                                                          
## factor(q_dur_distractor)0.5                                                           
## factor(q_dur_distractor)0.75                                                          
## chosenItemdistractor                                                               ***
## factor(condition)0.3                                                               ***
## factor(condition)0.5                                                               *  
## factor(condition)0.7                                                                  
## factor(condition)0.85                                                              .  
## factor(condition)0.95                                                              ***
## dur_target:factor(q_dur_distractor)0.25                                            .  
## dur_target:factor(q_dur_distractor)0.5                                             .  
## dur_target:factor(q_dur_distractor)0.75                                            ***
## dur_target:chosenItemdistractor                                                    *  
## factor(q_dur_distractor)0.25:chosenItemdistractor                                     
## factor(q_dur_distractor)0.5:chosenItemdistractor                                   .  
## factor(q_dur_distractor)0.75:chosenItemdistractor                                  .  
## dur_target:factor(condition)0.3                                                       
## dur_target:factor(condition)0.5                                                       
## dur_target:factor(condition)0.7                                                       
## dur_target:factor(condition)0.85                                                      
## dur_target:factor(condition)0.95                                                      
## factor(q_dur_distractor)0.25:factor(condition)0.3                                     
## factor(q_dur_distractor)0.5:factor(condition)0.3                                      
## factor(q_dur_distractor)0.75:factor(condition)0.3                                     
## factor(q_dur_distractor)0.25:factor(condition)0.5                                     
## factor(q_dur_distractor)0.5:factor(condition)0.5                                      
## factor(q_dur_distractor)0.75:factor(condition)0.5                                     
## factor(q_dur_distractor)0.25:factor(condition)0.7                                     
## factor(q_dur_distractor)0.5:factor(condition)0.7                                      
## factor(q_dur_distractor)0.75:factor(condition)0.7                                  .  
## factor(q_dur_distractor)0.25:factor(condition)0.85                                    
## factor(q_dur_distractor)0.5:factor(condition)0.85                                  *  
## factor(q_dur_distractor)0.75:factor(condition)0.85                                    
## factor(q_dur_distractor)0.25:factor(condition)0.95                                    
## factor(q_dur_distractor)0.5:factor(condition)0.95                                     
## factor(q_dur_distractor)0.75:factor(condition)0.95                                    
## chosenItemdistractor:factor(condition)0.3                                          .  
## chosenItemdistractor:factor(condition)0.5                                             
## chosenItemdistractor:factor(condition)0.7                                          *  
## chosenItemdistractor:factor(condition)0.85                                            
## chosenItemdistractor:factor(condition)0.95                                            
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor                          
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor                           
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor                          
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3                          
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3                           
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3                          
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5                          
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5                           
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5                          
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7                       *  
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7                           
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7                          
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85                         
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85                          
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85                         
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95                         
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95                       .  
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95                         
## dur_target:chosenItemdistractor:factor(condition)0.3                                  
## dur_target:chosenItemdistractor:factor(condition)0.5                                  
## dur_target:chosenItemdistractor:factor(condition)0.7                                  
## dur_target:chosenItemdistractor:factor(condition)0.85                                 
## dur_target:chosenItemdistractor:factor(condition)0.95                                 
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3                
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3                 
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3             .  
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5                
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5                 
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5             .  
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7                
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7                 
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7             *  
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85               
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85             ** 
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85               
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95               
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95                
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95               
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3     
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3   *  
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3     
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5     
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5   .  
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5     
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7     
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7      
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7     
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85    
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85  *  
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85    
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95    
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95     
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9421 on 55365 degrees of freedom
## Multiple R-squared:  0.1025, Adjusted R-squared:  0.1009 
## F-statistic: 66.53 on 95 and 55365 DF,  p-value: < 2.2e-16
Anova(f16)
## Anova Table (Type II tests)
## 
## Response: conf_normalized
##                                                                  Sum Sq    Df
## dur_target                                                          260     1
## factor(q_dur_distractor)                                            365     3
## chosenItem                                                         2584     1
## factor(condition)                                                  1542     5
## dur_target:factor(q_dur_distractor)                                 121     3
## dur_target:chosenItem                                                64     1
## factor(q_dur_distractor):chosenItem                                  44     3
## dur_target:factor(condition)                                         23     5
## factor(q_dur_distractor):factor(condition)                           83    15
## chosenItem:factor(condition)                                         63     5
## dur_target:factor(q_dur_distractor):chosenItem                       22     3
## dur_target:factor(q_dur_distractor):factor(condition)                44    15
## dur_target:chosenItem:factor(condition)                              18     5
## factor(q_dur_distractor):chosenItem:factor(condition)                41    15
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition)     65    15
## Residuals                                                         49140 55365
##                                                                    F value
## dur_target                                                        292.8945
## factor(q_dur_distractor)                                          136.9238
## chosenItem                                                       2911.8097
## factor(condition)                                                 347.4894
## dur_target:factor(q_dur_distractor)                                45.2985
## dur_target:chosenItem                                              71.9454
## factor(q_dur_distractor):chosenItem                                16.5135
## dur_target:factor(condition)                                        5.1288
## factor(q_dur_distractor):factor(condition)                          6.2384
## chosenItem:factor(condition)                                       14.2757
## dur_target:factor(q_dur_distractor):chosenItem                      8.1615
## dur_target:factor(q_dur_distractor):factor(condition)               3.2804
## dur_target:chosenItem:factor(condition)                             3.9489
## factor(q_dur_distractor):chosenItem:factor(condition)               3.0576
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition)    4.9020
## Residuals                                                                 
##                                                                     Pr(>F)    
## dur_target                                                       < 2.2e-16 ***
## factor(q_dur_distractor)                                         < 2.2e-16 ***
## chosenItem                                                       < 2.2e-16 ***
## factor(condition)                                                < 2.2e-16 ***
## dur_target:factor(q_dur_distractor)                              < 2.2e-16 ***
## dur_target:chosenItem                                            < 2.2e-16 ***
## factor(q_dur_distractor):chosenItem                              1.011e-10 ***
## dur_target:factor(condition)                                     0.0001048 ***
## factor(q_dur_distractor):factor(condition)                       2.189e-13 ***
## chosenItem:factor(condition)                                     5.406e-14 ***
## dur_target:factor(q_dur_distractor):chosenItem                   1.984e-05 ***
## dur_target:factor(q_dur_distractor):factor(condition)            1.632e-05 ***
## dur_target:chosenItem:factor(condition)                          0.0013972 ** 
## factor(q_dur_distractor):chosenItem:factor(condition)            5.616e-05 ***
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition) 1.060e-09 ***
## Residuals                                                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f16, terms = c("dur_target", "q_dur_distractor", "chosenItem", "condition")))

first fixation item

dat %>%
    group_by(subj, condition, chosenItem, firstFixItem) %>%
    summarise(n = n()) %>%
    ungroup(subj, condition, chosenItem) %>%
    complete(subj, condition, chosenItem) %>%
    ggplot(., aes(x = as.numeric(as.character(condition)), y = n, color = firstFixItem)) + 
    geom_point() + stat_summary(fun.y = "mean", geom = "line") + ggtitle("Number of first fixation")
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
## Warning: Removed 34 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 34 rows containing missing values (`geom_point()`).

first fixation item (choice considered)

dat %>%
    group_by(subj, condition, chosenItem, firstFixItem) %>%
    summarise(n = n()) %>%
    ungroup(subj, condition, chosenItem) %>%
    complete(subj, condition, chosenItem) %>%
    ggplot(., aes(x = as.numeric(as.character(condition)), y = n, color = firstFixItem)) + 
    geom_point() + stat_summary(fun.y = "mean", geom = "line") + ggtitle("Number of first fixation") + facet_wrap(. ~ chosenItem)
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
## Warning: Removed 34 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 34 rows containing missing values (`geom_point()`).

gaze shift (この三変数を使ってチョイスを予測)

hist(dat$gazeShift_total)

dat %>%
    group_by(subj, condition) %>%
    summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
              m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
              m_gazeShift_target_dud = mean(gazeShift_target_dud),
              m_gazeShift_total = mean(gazeShift_total)) %>%
    ungroup(subj, condition) %>%
    complete(subj, condition) %>%
    mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj'. You can override using the
## `.groups` argument.
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar")
## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar")

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar")

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar")

gaze shift (choice considered)

dat %>%
    group_by(subj, chosenItem, condition) %>%
    summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
           m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
           m_gazeShift_target_dud = mean(gazeShift_target_dud),
           m_gazeShift_total = mean(gazeShift_total)) %>%
    ungroup(subj, chosenItem, condition) %>%
    complete(subj, chosenItem, condition) %>%
    mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'chosenItem'. You can override
## using the `.groups` argument.
## Warning in `[<-.factor`(`*tmp*`, list, value = 0): 不正な因子水準です。NA
## が発生しました
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)

gaze shift (confidence considered)

dat %>%
    group_by(subj, conf, condition) %>%
    summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
              m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
              m_gazeShift_target_dud = mean(gazeShift_target_dud),
              m_gazeShift_total = mean(gazeShift_total)) %>%
    ungroup(subj, conf, condition) %>%
    complete(subj, conf, condition) %>%
    mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'conf'. You can override using the
## `.groups` argument.
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)
## Warning: Removed 9 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 9 rows containing missing values (`geom_point()`).

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)

gaze shift (choice and confidence considered)

dat %>%
    group_by(subj, chosenItem, conf, condition) %>%
    summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
              m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
              m_gazeShift_target_dud = mean(gazeShift_target_dud),
              m_gazeShift_total = mean(gazeShift_total)) %>%
    ungroup(subj, chosenItem, conf, condition) %>%
    complete(subj, chosenItem, conf, condition) %>%
    mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'chosenItem', 'conf'. You can
## override using the `.groups` argument.
## Warning in `[<-.factor`(`*tmp*`, list, value = 0): 不正な因子水準です。NA
## が発生しました
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)
## Warning: Removed 25 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 25 rows containing missing values (`geom_point()`).

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)

ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) + 
    geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
    stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)